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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 15 — Jul. 16, 2012
  • pp: 16838–16860
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Novel rotation algorithm for phase unwrapping applications

Jing-Feng Weng and Yu-Lung Lo  »View Author Affiliations


Optics Express, Vol. 20, Issue 15, pp. 16838-16860 (2012)
http://dx.doi.org/10.1364/OE.20.016838


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Abstract

In most phase unwrapping algorithms, the image reconstruction results are obtained by shifting the phase jumps in the wrapped phase map by 2π. The performance of such algorithms is degraded by the presence of speckle noise, residual noise, noise at the height discontinuities and holes in the wrapped phase map. Thus, a filtering operation is performed prior to the unwrapping process in order to remove the noise. However, the filtering process smears the phase jumps in the wrapped phase map and therefore causes a phase shifting error during the reconstruction process. Moreover, the noise errors, hole errors and shifting errors are accumulated path-by-path during unwrapping. Accordingly, the present study proposes a new rotation algorithm for phase unwrapping applications which resolves the noise error, the error of hole, the shifting error. Existing phase unwrapping algorithms are designed to operate only on those pixels whose phase values have no noise or holes. Or they are designed to operate the three-dimensional unwrapping paths in the row and column directions to avoid the noise or holes. By contrast, the rotation algorithm proposed in this study operates on all the pixels in the wrapped phase map, including those affected by noise or holes. As a result, the noise errors and hole errors produced in existing 2π phase shifting unwrapping algorithms are eliminated. Furthermore, since in the proposed approach, the wrapped phase map is not filtered prior to the unwrapping process, the phase shifting errors induced in existing algorithms are also eliminated. The robustness of the proposed algorithm to various noise errors, hole errors and phase shifting errors is demonstrated both numerically and experimentally.

© 2012 OSA

1. Introduction

In optical interferometry, the quality of the 2D or 3D reconstruction results is seriously degraded by the presence of noise in the wrapped phase map. In general, this noise includes speckle noise [1

1. B. F. Pouet and S. Krishnaswamy, “Technique for the removal of speckle phase in electronic speckle interferometry,” Opt. Lett. 20(3), 318–320 (1995). [CrossRef] [PubMed]

3

3. M. J. Huang and J. K. Liou, “Retrieving ESPI map of discontinuous objects via a novel phase unwrapping algorithm,” Strain 44(3), 239–247 (2008). [CrossRef]

], residual noise [4

4. R. Yamaki and A. Hirose, “Singularity-spreading phase unwrapping,” IEEE Trans. Geosci. Rem. Sens. 45(10), 3240–3251 (2007). [CrossRef]

] and noise at the height discontinuities of the imaged object [5

5. E. H. Kim, J. Hahn, H. Kim, and B. Lee, “Profilometry without phase unwrapping using multi-frequency and four-step phase-shift sinusoidal fringe projection,” Opt. Express 17(10), 7818–7830 (2009). [CrossRef] [PubMed]

9

9. A. Wada, M. Kato, and Y. Ishii, “Large step-height measurements using multiple-wavelength holographic interferometry with tunable laser diodes,” J. Opt. Soc. Am. A 25(12), 3013–3020 (2008). [CrossRef] [PubMed]

]. Speckle noise is usually produced by the laser source used for illumination purposes, while residual noise is caused by a contamination of either the sample or the optical system. Finally, noise at the lateral surfaces arises due to the inherent diffraction and depth-of-field limitations of the optical system [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

12

12. J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49(11), 2143–2150 (2010). [CrossRef] [PubMed]

]. Speckle noise is generally removed using digital methods [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

13

13. H. A. Aebischer and S. Waldner, “A simple and effective method for filtering speckle-interferometric phase fringe patterns,” Opt. Commun. 162(4–6), 205–210 (1999). [CrossRef]

] or by using a white light source or spatial filter in the experimental configuration [1

1. B. F. Pouet and S. Krishnaswamy, “Technique for the removal of speckle phase in electronic speckle interferometry,” Opt. Lett. 20(3), 318–320 (1995). [CrossRef] [PubMed]

,2

2. I. Moon and B. Javidi, “Three-dimensional speckle-noise reduction by using coherent integral imaging,” Opt. Lett. 34(8), 1246–1248 (2009). [CrossRef] [PubMed]

]. Meanwhile, the effects of residual noise and noise at the height discontinuities are resolved by designing the phase unwrapping algorithm to avoid noisy pixels in the wrapped phase map [14

14. H. O. Saldner and J. M. Huntley, “Temporal phase unwrapping: application to surface profiling of discontinuous objects,” Appl. Opt. 36(13), 2770–2775 (1997). [CrossRef] [PubMed]

,15

15. S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express 15(13), 8059–8064 (2007). [CrossRef] [PubMed]

] or by using a digital filtering scheme [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

12

12. J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49(11), 2143–2150 (2010). [CrossRef] [PubMed]

,16

16. A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36(9), 2466–2472 (1997). [CrossRef]

].

Generally speaking, existing phase unwrapping algorithms can be classified as either temporal [14

14. H. O. Saldner and J. M. Huntley, “Temporal phase unwrapping: application to surface profiling of discontinuous objects,” Appl. Opt. 36(13), 2770–2775 (1997). [CrossRef] [PubMed]

,17

17. J. M. Huntley and H. Saldner, “Temporal phase-unwrapping algorithm for automated interferogram analysis,” Appl. Opt. 32(17), 3047–3052 (1993). [CrossRef] [PubMed]

,18

18. D. S. Mehta, S. K. Dubey, M. M. Hossain, and C. Shakher, “Simple multifrequency and phase-shifting fringe-projection system based on two-wavelength lateral shearing interferometry for three-dimensional profilometry,” Appl. Opt. 44(35), 7515–7521 (2005). [CrossRef] [PubMed]

], spatial [15

15. S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express 15(13), 8059–8064 (2007). [CrossRef] [PubMed]

,19

19. S. Zhang, X. L. Li, and S. T. Yau, “Multilevel quality-guided phase unwrapping algorithm for real-time three-dimensional shape reconstruction,” Appl. Opt. 46(1), 50–57 (2007). [CrossRef] [PubMed]

26

26. T. J. Flynn, “Two-dimensional phase unwrapping with minimum weighted discontinuity,” J. Opt. Soc. Am. A 14(10), 2692–2701 (1997). [CrossRef]

], or period-coding [27

27. M. A. Navarro, J. C. Estrada, M. Servin, J. A. Quiroga, and J. Vargas, “Fast two-dimensional simultaneous phase unwrapping and low-pass filtering,” Opt. Express 20(3), 2556–2561 (2012). [CrossRef] [PubMed]

]. Spatial phase unwrapping algorithms can be further classified as path-dependent [20

20. W. W. Macy Jr., “Two-dimensional fringe-pattern analysis,” Appl. Opt. 22(23), 3898–3901 (1983). [CrossRef] [PubMed]

], path-independent [21

21. D. C. Ghiglia, G. Mastin, and L. A. Romero, “Cellular-automata method for phase unwrapping,” J. Opt. Soc. Am. A 4(1), 267–280 (1987). [CrossRef]

23

23. H. Y. Chang, C. W. Chen, C. K. Lee, and C. P. Hu, “The Tapestry Cellular Automata phase unwrapping algorithm for interferogram analysis,” Opt. Lasers Eng. 30(6), 487–502 (1998). [CrossRef]

], or miscellaneous [15

15. S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express 15(13), 8059–8064 (2007). [CrossRef] [PubMed]

,19

19. S. Zhang, X. L. Li, and S. T. Yau, “Multilevel quality-guided phase unwrapping algorithm for real-time three-dimensional shape reconstruction,” Appl. Opt. 46(1), 50–57 (2007). [CrossRef] [PubMed]

,24

24. K. A. Stetson, J. Wahid, and P. Gauthier, “Noise-immune phase unwrapping by use of calculated wrap regions,” Appl. Opt. 36(20), 4830–4838 (1997). [CrossRef] [PubMed]

31

31. J. J. Martinez-Espla, T. Martinez-Marin, and J. M. Lopez-Sanchez, “Using a Grid-Based Filter to Solve InSAR Phase Unwrapping,” IEEE Geosci. Remote Sens. 5(2), 147–151 (2008). [CrossRef]

]. In every case, the unwrapping process involves shifting the phase jumps in the wrapped phase map by 2π. First, MACY [20

20. W. W. Macy Jr., “Two-dimensional fringe-pattern analysis,” Appl. Opt. 22(23), 3898–3901 (1983). [CrossRef] [PubMed]

], a well-known path-dependent algorithm, has a fast runtime. It operates the phase shifting by 2π when the difference between two neighboring pixels is greater than π. Therefore, if the noise is greater than π, MACY treats the noise as the phase jumps, and thus the unwrapping result fails. Second, the cellular automata (CA) algorithm [21

21. D. C. Ghiglia, G. Mastin, and L. A. Romero, “Cellular-automata method for phase unwrapping,” J. Opt. Soc. Am. A 4(1), 267–280 (1987). [CrossRef]

23

23. H. Y. Chang, C. W. Chen, C. K. Lee, and C. P. Hu, “The Tapestry Cellular Automata phase unwrapping algorithm for interferogram analysis,” Opt. Lasers Eng. 30(6), 487–502 (1998). [CrossRef]

], a path independent algorithm, yields a better unwrapping performance than MACY since it operates the 3D unwrapping paths in row and column directions to avoid the noisy pixels in the wrapped phase map. However, CA is time-consuming and may fail to converge if the wrapped phase map includes noise which is greater than π [11

11. J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express 20(10), 10896–10920 (2012). [CrossRef] [PubMed]

,12

12. J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49(11), 2143–2150 (2010). [CrossRef] [PubMed]

,24

24. K. A. Stetson, J. Wahid, and P. Gauthier, “Noise-immune phase unwrapping by use of calculated wrap regions,” Appl. Opt. 36(20), 4830–4838 (1997). [CrossRef] [PubMed]

]. Finally, in order to resolve one of three types of noise (i.e. speckle noise, residual noise, and noise at the height discontinuities), the miscellaneous algorithms are continually presented [15

15. S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express 15(13), 8059–8064 (2007). [CrossRef] [PubMed]

,24

24. K. A. Stetson, J. Wahid, and P. Gauthier, “Noise-immune phase unwrapping by use of calculated wrap regions,” Appl. Opt. 36(20), 4830–4838 (1997). [CrossRef] [PubMed]

31

31. J. J. Martinez-Espla, T. Martinez-Marin, and J. M. Lopez-Sanchez, “Using a Grid-Based Filter to Solve InSAR Phase Unwrapping,” IEEE Geosci. Remote Sens. 5(2), 147–151 (2008). [CrossRef]

]. As with CA, the robust spinning algorithm [15

15. S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express 15(13), 8059–8064 (2007). [CrossRef] [PubMed]

] also operates suitable unwrapping paths in row and column directions to avoid the noisy pixels (i.e. residual noise) in the wrapped phase map. Compared to the CA algorithm, the spinning algorithm achieves an improved unwrapping performance since its evaluation is faster and smarter to select the correct and non-noisy paths. The branch cut is the common method applied to the phase unwrapping algorithm with the noise problems [25

25. R. Goldstein, H. Zebker, and C. Werner, “Satellite radar interferometry: two-dimensional phase unwrapping,” Radio Sci. 23(4), 713–720 (1988). [CrossRef]

,26

26. T. J. Flynn, “Two-dimensional phase unwrapping with minimum weighted discontinuity,” J. Opt. Soc. Am. A 14(10), 2692–2701 (1997). [CrossRef]

]. Similarly, the inconsistent pixels marked by the branch cut can be avoided by the corresponding phase unwrapping algorithms. Nevertheless, the high speckle or high residual noise greater than π still easily causes the branch cut to be invalid [27

27. M. A. Navarro, J. C. Estrada, M. Servin, J. A. Quiroga, and J. Vargas, “Fast two-dimensional simultaneous phase unwrapping and low-pass filtering,” Opt. Express 20(3), 2556–2561 (2012). [CrossRef] [PubMed]

]. Also, the noise at the height discontinuities easily causes the branch cut to fail [28

28. H. Cui, W. Liao, N. Dai, and X. Cheng, “Reliability-guided phase-unwrapping algorithm for the measurement of discontinuous three-dimensional objects,” Opt. Eng. 50(6), 063602 (2011). [CrossRef]

]. Accordingly, in order to reduce the influence of the high speckle noise, the phase unwrapping and linear-filtering algorithms are performed simultaneously [27

27. M. A. Navarro, J. C. Estrada, M. Servin, J. A. Quiroga, and J. Vargas, “Fast two-dimensional simultaneous phase unwrapping and low-pass filtering,” Opt. Express 20(3), 2556–2561 (2012). [CrossRef] [PubMed]

,29

29. J. C. Estrada, M. Servin, and J. Vargas, “2D simultaneous phase unwrapping and filtering: a review and comparison,” Opt. Eng. 50(8), 1026–1029 (2011).

31

31. J. J. Martinez-Espla, T. Martinez-Marin, and J. M. Lopez-Sanchez, “Using a Grid-Based Filter to Solve InSAR Phase Unwrapping,” IEEE Geosci. Remote Sens. 5(2), 147–151 (2008). [CrossRef]

]. Similarly, the noise at height discontinuities is discussed by the method of minimum weighted discontinuity, which operates the paths in row and column directions to find the discontinuities containing the minimum weighted sum [26

26. T. J. Flynn, “Two-dimensional phase unwrapping with minimum weighted discontinuity,” J. Opt. Soc. Am. A 14(10), 2692–2701 (1997). [CrossRef]

]. Furthermore, the speckle noise and noise at height discontinuities are simultaneously discussed in [24

24. K. A. Stetson, J. Wahid, and P. Gauthier, “Noise-immune phase unwrapping by use of calculated wrap regions,” Appl. Opt. 36(20), 4830–4838 (1997). [CrossRef] [PubMed]

], which can find the correct phase jumps located in the interval of calculated wrap regions.

As described above [15

15. S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express 15(13), 8059–8064 (2007). [CrossRef] [PubMed]

,24

24. K. A. Stetson, J. Wahid, and P. Gauthier, “Noise-immune phase unwrapping by use of calculated wrap regions,” Appl. Opt. 36(20), 4830–4838 (1997). [CrossRef] [PubMed]

,26

26. T. J. Flynn, “Two-dimensional phase unwrapping with minimum weighted discontinuity,” J. Opt. Soc. Am. A 14(10), 2692–2701 (1997). [CrossRef]

], unfortunately, however, they can only be applied to 3D wrapped phase maps. That is, in a 3D wrapped phase map, each coordinate comprises row, column and phase information, and thus the unwrapping algorithm can avoid noise in either the row direction or the column direction. Importantly, the unwrapping algorithm does not work effectively if it does not find the correct and non-noisy paths in the row and column directions simultaneously. By contrast, in a 2D wrapped phase map, each coordinate comprises only row and phase information. In other words, the unwrapping algorithm can only use a row unwrapping path. Thus, if this path contains noisy pixels, a reconstruction error inevitably occurs.

The performance of existing phase unwrapping algorithms is commonly improved by using a filter to remove the noise from the wrapped phase map prior to unwrapping. Filtering algorithms can be broadly classified as either linear [13

13. H. A. Aebischer and S. Waldner, “A simple and effective method for filtering speckle-interferometric phase fringe patterns,” Opt. Commun. 162(4–6), 205–210 (1999). [CrossRef]

] or nonlinear [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

12

12. J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49(11), 2143–2150 (2010). [CrossRef] [PubMed]

,16

16. A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36(9), 2466–2472 (1997). [CrossRef]

]. Unfortunately, both types of algorithm smear the phase jumps in the wrapped phase map, and therefore result in a phase shifting error during the unwrapping process. Generally speaking, the smearing effect induced by linear filters is worse than that induced by nonlinear filters. For both types of filter, the smearing effect is reduced if the noise is uniformly distributed in the wrapped phase map (i.e., as in the case of speckle noise) [13

13. H. A. Aebischer and S. Waldner, “A simple and effective method for filtering speckle-interferometric phase fringe patterns,” Opt. Commun. 162(4–6), 205–210 (1999). [CrossRef]

,16

16. A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36(9), 2466–2472 (1997). [CrossRef]

]. However, if the noise is localized only in certain regions of the wrapped phase map (e.g., as in the case of residual noise or noise at the height discontinuities), a significant smearing effect is induced. As a result, a requirement exists for smart nonlinear filters capable of removing such noise without an excessive smearing of the phase jumps in the wrapped phase map [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

12

12. J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49(11), 2143–2150 (2010). [CrossRef] [PubMed]

].

In addition to speckle noise, residual noise and noise at the height discontinuities of the scanned object, the wrapped phase map may also include “holes”. That is, regions of the phase map in which the pixels have a very low value. In practice, these holes correspond to concave features on the surface of the scanned object, e.g., holes, dips, grooves, and so on. As with the serious noise greater than π, the values of holes greater than π also cause most existing phase unwrapping algorithms to fail. As a result, in most existing phase unwrapping algorithms based on 2π shifts of the phase jumps, the reconstruction results are highly sensitive to the effects of noise, phase shifting errors and holes. In addition, although the filtering algorithm can effectively remove the noise, it also destroys the real data in the holes.

Accordingly, this study proposes a novel rotation unwrapping algorithm which operates not only on those pixels with correct phase values, but also on those pixels whose phase values are corrupted by noise or hole errors. In other words, in contrast to existing methods, a filtering operation is not performed prior to the unwrapping process. Rather, the effects of noise and hole errors are eliminated by optionally applying a filter to the reconstruction results after the rotation algorithm has terminated. Importantly, the proposed rotation algorithm is not based on shifting 2π phase jumps. As a result, the phase shifting error induced in existing unwrapping methods is also eliminated. Before performing the proposed rotation algorithm, the phase jump pixels are detected by the detection scheme [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

], and then the missed phase jump pixels are detected by the additional phase jump detection scheme. The proposed rotation algorithm contains two parts, the relative rotation procedure and the absolute rotation procedure. The relative rotation procedure operates 2D reconstruction and is introduced in this study. The absolute rotation procedure will be introduced in the future to operate the 3D reconstruction. The remainder of this paper is organized as follows. Section 2 introduces the basic principles of the proposed rotation algorithm. Section 3 describes the functional robustness of the proposed algorithm toward noise errors, hole errors and shifting errors. Sections 4 and 5 present the simulation and experimental results, respectively. Finally, Section 6 presents some brief concluding remarks.

2. Principles of proposed rotation algorithm

As described in Introduction, the noise and phase jumps in the wrapped phase map are detected before the rotation unwrapping process using the scheme proposed in [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

].

2.1 Two Detection schemes for phase jumps

2.1.1 Noise and phase jump detection scheme

In accordance with the method proposed in [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

], the noise and phase jump positions in the wrapped phase map are detected by means of four parameters, S1, S2, S3 and S4, defined as
S1(i,j)=[ϕ(i+1,j)ϕ(i,j)σΑ2π]+[ϕ(i+1,j+1)ϕ(i+1,j)+σΑ2π]+[ϕ(i,j+1)ϕ(i+1,j+1)σΑ2π]+[ϕ(i,j)ϕ(i,j+1)+σΑ2π]S2(i,j)=[ϕ(i+1,j)ϕ(i,j)+σΑ2π]+[ϕ(i+1,j+1)ϕ(i+1,j)σΑ2π]+[ϕ(i,j+1)ϕ(i+1,j+1)+σΑ2π]+[ϕ(i,j)ϕ(i,j+1)σΑ2π]S3(i,j)=[ϕ(i+1,j)ϕ(i,j)+σΑ2π]+[ϕ(i+1,j+1)ϕ(i+1,j)+σΑ2π]+[ϕ(i,j+1)ϕ(i+1,j+1)σΑ2π]+[ϕ(i,j)ϕ(i,j+1)σΑ2π]S4(i,j)=[ϕ(i+1,j)ϕ(i,j)σΑ2π]+[ϕ(i+1,j+1)ϕ(i+1,j)σΑ2π]+[ϕ(i,j+1)ϕ(i+1,j+1)+σΑ2π]+[ϕ(i,j)ϕ(i,j+1)+σΑ2π]
(1)
where (i, j) is the current pixel position; ϕ is the corresponding phase value in the phase map; [ ] indicates a rounding operation; and σΑ is the noise and phase jump detection threshold parameter, which is in the range of 0<σΑ<π. It is noted that Eq. (1) operates the noise and phase jump positions in the 3D coordinate, composed of the row pixel, the column pixel, and the phase. Meanwhile, in the 2D coordinate, the column pixel of j+1 is same as the column pixel of j in Eq. (1). For the sake of simplicity, the column pixel equal to 1 is chosen as a case in the 2D coordinate, thus Eq. (1) can be expressed as
S1(i,1)=[ϕ(i+1,1)ϕ(i,1)σΑ2π]+ϕ(i,1)ϕ(i+1,1)σΑ2π]S2(i,1)=[ϕ(i+1,1)ϕ(i,1)+σΑ2π]+[ϕ(i,1)ϕ(i+1,1)+σΑ2π]S3(i,1)=[ϕ(i+1,1)ϕ(i,1)+σΑ2π]+[ϕ(i,1)ϕ(i+1,1)σΑ2π]S4(i,1)=[ϕ(i+1,1)ϕ(i,1)σΑ2π]+[ϕ(i,1)ϕ(i+1,1)+σΑ2π]
(2)
In Eq. (2), the noise and phase jump detection threshold parameter of σΑ is still in the range of 0<σΑ<π. In [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

], πσΑ is defined as the absolute-maximum phase difference in the whole phase map. Meanwhile, |PD| is the absolute phase difference between any two neighboring pixels, i.e. ϕ(i+1,j)ϕ(i,j), ϕ(i+1,j+1)ϕ(i+1,j),  ϕ(i,j+1)ϕ(i+1,j+1), and  ϕ(i,j)ϕ(i,j+1). Comparing the magnitudes of |PD| and πσΑ, all of pixels can be classified as the bad pixels (i.e. noise), the good pixels, and the phase jump pixels, i.e.,
  • (1) Bad pixel (i.e. noise): the current pixel is designated as a “bad” pixel if |PD| is greater than or equal to πσΑ. In Eq. (1) and Eq. (2), a bad pixel causes one or more of parameters S1, S2, S3 or S4 to have a value other than zero.
  • (2) Good pixel: the current pixel is designated as a “good” pixel if |PD| is less than πσΑ. In Eq. (1) and Eq. (2), a good pixel yields a result of S1(i,j)=S2(i,j)=S3(i,j)=S4(i,j)=0
  • (3) Phase jump pixel: the current pixel is defined as a “phase jump” pixel if one or more of S1-S4 is not equal to zero, but the sum of S1-S4 is equal to zero.
In 3D coordinate, all of the pixels detected by Eq. (1) are marked in two maps, namely a noise map showing the positions of all the bad pixels and a phase jump map showing the positions of all the phase jumps. However, in 2D coordinate, the bad and phase jump pixels detected by Eq. (2) are directly marked in the wrapped phase for inspecting the noise and phase jump easily. After a suitable threshold parameter setting of σΑ is properly decided, both of the phase jump positions and the noise positions are correctly detected.

2.1.2 Additional phase jump detection scheme for the proposed rotation algorithm

One phase jump should contain one 2π-high-position pixel and one 0-low-position pixel. Unfortunately, only one high- or low- position pixel in one phase jump is detected by Eq. (1) or Eq. (2). In this study, another missed phase jump can be obtained by the first term in S1(i,j) in Eq. (1) or the first term in S1(i,1) in Eq. (2). It is noted that if the 2π-high-position pixel of ϕ(i,j) is detected by Eq. (1), the corresponding 0-low-position pixel of ϕ(i+1,j) yields [ϕ(i+1,j)ϕ(i,j)σΑ2π]=1. By contract, if the 0-low-position pixel of ϕ(i,j) is detected by Eq. (1), the corresponding 2π-high-position pixel of ϕ(i+1,j) yields [ϕ(i+1,j)ϕ(i,j)σΑ2π]=1. Similarly, in the case of the 2π-high-position pixel of ϕ(i,1) and the corresponding 0-low-position pixel of ϕ(i+1,1), [ϕ(i+1,1)ϕ(i,1)σΑ2π]=1 is obtained. And in the case of the 0-low-position pixel of ϕ(i,1) and the corresponding 2π-high-position pixel of ϕ(i+1,1), [ϕ(i+1,1)ϕ(i,1)σΑ2π]=1 is obtained. It is concluded that the following Eq. (3) and Eq. (4) present the corresponding phase jumps (i.e. 2π-high-position or 0-low-position) by using the first term in S1(i,j) in Eq. (1) in 3D coordinate and the first term in S1(i,1) in Eq. (2) in 2D coordinate, respectively.

For 3D coordinate:
{Pixel (i+1,j) is a 0-low phase jump position :if [ϕ(i+1,j)ϕ(i,j)σΑ2π]=1Pixel (i+1,j) is a 2π-high phase jump position:if [ϕ(i+1,j)ϕ(i,j)σΑ2π]=1
(3)
For 2D coordinate:
{Pixel (i+1,1) is a 0-low phase jump position :if  [ϕ(i+1,1)ϕ(i,1)σΑ2π]=1Pixel (i+1,1) is a 2π-high phase jump position:if [ϕ(i+1,1)ϕ(i,1)σΑ2π]=1
(4)
It is noted that the difference between the two neighboring pixels (pixels ϕ(i,j) and ϕ(i+1,j), or pixels ϕ(i,1) and ϕ(i+1,1)) from Eq. (3) and Eq. (4) is approximate to 2π or −2π. This value of difference yields a more efficient detection result by using the threshold parameter setting of σΑ. Therefore, the additional phase jump detection scheme introduced in this study can detect another missed pixel in one phase jump, which is improved from the detection scheme in [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

].

2.2 Relative rotation scheme

Figure 1
Fig. 1 2D wrapped phase. Note that the blue-solid lines represent wrapped phase lines. Note also that Phase 1 extends from a point to the left of D2 to D2 while Phase 2 extends fully from D3 to D4. Finally, note that the red triangles represent noise or holes.
shows a typical 2D wrapped phase map (i.e., row vs. phase). As shown, the map contains two 2π phase jumps. Pixels D2 and D4 are located at the highest extremities of the 2π phase jumps, while pixels D1 and D3 are located at the lowest extremities of the 2π phase jumps. Phase 1 contains a subset of the phase values, noise and holes which in its entirety contains D2 and all of the phase values, noise and holes to the left of D2. Meanwhile, Phase 2 extends fully from D3 to D4. C1 and C2 denote the proposed-evaluating centers of Phase 1 and Phase 2, respectively, will be introduced later. It is seen that a height discontinuity exists between C1 and D2. Finally, the red triangles represent noise or holes. (Note that in Fig. 1, the row pixel coordinates are integral.)

The relative rotation scheme in the proposed unwrapping algorithm comprises three steps, namely a first relative rotation procedure, a second relative rotation procedure, and a stitching procedure. The first relative rotation procedure separately rotates all of the phase values (including those affected by noise and holes) in Phase 1 and Phase 2, respectively. In the second relative rotation procedure, all of values in Phase 1 are rotated while those in Phase 2 are kept unchanged. Finally, in the third step, Phase 1 and Phase 2 obtained in the second relative rotation procedure are stitched together in order to produce the final 2D reconstruction results.

2.2.1 First relative rotation procedure

As described above, from D1 to D2 (or from D3 to D4), the phase values, and noise and holes associated with each pixel are used to determine the center position in the row direction and corresponding phase center value in accordance with Eq. (5) and Eq. (6), respectively. That is,
row_center1=i=D1D2phase(i,1)*ii=D1D2phase(i,1)orrow_center2=i=D3D4phase(i,1)*ii=D3D4phase(i,1)
(5)
where row_center1 represents the row center position located on Phase 1; i(=D1 to D2) is the row position between D1 and D2; similarly, row_center2 represents the row center position located on Phase 2; i(=D3 to D4) is the row position between D3 and D4. And phase(i,1) denotes the phase or noise or hole value between D1 and D2, or between D3 and D4. Having obtained the row center position from Eq. (5), the phase center value is determined as
phase_center1=interpolation with row_center1                                and two neighboring integral row pixelsorphase_center2=interpolation with row_center2                                 and two neighboring integral row pixels
(6)
where phase_center1 (or phase_center2) denotes the phase value corresponding to the row center of row_center1 (or row_center2), and is determined via an interpolation process based on the row center pixel and the two neighboring integral row pixels. Note that the phase values, holes and noise are all preserved in Eq. (5) and Eq. (6). (Note also that this approach differs from that in traditional phase unwrapping algorithms in which the wrapped phase map is filtered to remove noise and hole errors prior to the unwrapping process.)

The basic steps in the first relative rotation procedure are as follows:

  • i. Determine the two center pixels C1 and C2: The positions and phase values of pixels C1 and C2, corresponding to the center positions of Phase 1 and Phase 2, respectively, are determined using Eq. (5) and Eq. (6), and expressed as C1(row_center1,phase_center1) and C2(row_center2,phase_center2) (row, phase).
  • ii. Determine vectors D1D2, D3D4 and C1C2: Pixels D1, D2, D3, D4, C1 and C2 are all known. Thus, the three vectors D1D2, D3D4 and C1C2 can be directly obtained.
  • iii. Determine the angle θfix between vectorsD1D2 and D3D4: As shown in Fig. 1, vectors D1D2 and D3D4 have an angle θfix between them before the first relative rotation operation is performed. When the first and second relative rotation procedures have both been completed, the value of θfix is unchanged (see the related discussions in the section 3 and the subsection 4.5). The value of θfix is recorded in the first relative rotation operation, and is then used in the second relative rotation operation.
  • iv. Determine θ1 from D1D2 and C1C2, and θ2 from D3D4 and C1C2: The angles θ1 and θ2 between vectors D1D2 and C1C2 and vectors D3D4and C1C2, respectively, are determined.
  • v. Rotate Phase 2 through angle θ2 and then rotate Phase 1 through angle θ1: Phase 2 is rotated through an angle θ2 about center pixel C2 in a clockwise direction (i.e., toward vector C1C2). Similarly, Phase 1 is rotated through an angle θ1 about center pixel C1 toward vector C1C2. (Note that the clockwise direction is defined as positive rotation, and is denoted as θ1+ and θ2+ in Fig. 1).
  • vi. Complete the first relative rotation procedure: The first relative rotation procedure terminates once Phase 1 and Phase 2 have been rotated through angles θ1 and θ2 toward vector C1C2, respectively. Following rotation through θ2, the position of Phase 2 shifts from pixels D3 and D4 (see Fig. 1) to new pixels D3′ and D4’ (see blue line in Fig. 2
    Fig. 2 Results of first relative rotation procedure. Note that the red line indicates the result obtained by rotating Phase 1 about C1 byθ1, while the blue line indicates the result obtained by rotating Phase 2 about C2 byθ2. Note also that the phase values, noise and holes are all preserved in the rotation results.
    ). Similarly, following rotation through θ1, Phase 1 shifts from pixels D1 and D2 to new pixels D1’ and D2’(see red line in Fig. 2).
    Fig. 2Results of first relative rotation procedure. Note that the red line indicates the result obtained by rotating Phase 1 about C1 byθ1, while the blue line indicates the result obtained by rotating Phase 2 about C2 byθ2. Note also that the phase values, noise and holes are all preserved in the rotation results.

2.2.2 Second relative rotation procedure

A second relative rotation procedure is performed to further rotate Phase 1 for corresponding to the known and correct angle of θfix.

The basic steps in the second relative rotation procedure are as follows:

  • vii. Record the angle θ'fix between the two vectors D1'D2' andD3'D4': In Fig. 2, the positions of pixels D1’, D2’, D3′ and D4’ are all known following the first relative rotation operation. The angle θ'fix between the vectors D1'D2' and D3'D4' can thus be directly determined. Note that in Figs. 1 and 2, the positions of C1 and C2 are unchanged since pixels C1 and C2 serve as the centers of rotation for Phase 1 and Phase 2, respectively, in the first relative rotation operation. As a result, vector C1C2 is also unchanged following the first rotation procedure.
  • viii. Rotate Phase 1 though angle θ''fix about fixed rotating center C1: The angle θfix is already known from Step (iii). The angle θ''fix through which Phase 1 is rotated about rotating center C1. As described in the following, the value of θ''fix can be determined directly from the known angles θfix and θ'fix since all three angles are based on the unchanged vector C1C2. Specifically, if the tangent function of unit vector of D1D2 is greater than that of unit vector of D3D4 at D2’, then θ''fix is obtained as θ''fix=θ'fixθfix. Conversely, if the tangent function of unit vector of D1D2 is less than or equal to that of unit vector of D3D4 at D2’, then θ''fix=θfixθ'fix. Having determined the value of θ''fix, Phase 1 is rotated about pixel C1 through an angle θ''fix, while Phase 2 is unchanged. Following the rotation process, the position of Phase 1 moves from pixels D1’ to D2’ (see Fig. 2) to new pixels D1” to D2”, as shown in Fig. 3
    Fig. 3 Results of second relative rotation procedure in which rotated Phase 1 from D1’ to D2’ is further rotated by θ''fix leading to new rotated Phase 1 from D1” to D2”. A stitching process is then performed in which new rotated Phase 1 is shifted in the y-direction and stitched to Phase 2 from D3′ to D4’.
    .
    Fig. 3Results of second relative rotation procedure in which rotated Phase 1 from D1’ to D2’ is further rotated by θ''fix leading to new rotated Phase 1 from D1” to D2”. A stitching process is then performed in which new rotated Phase 1 is shifted in the y-direction and stitched to Phase 2 from D3′ to D4’.

2.2.3 Stitching process

Following the second relative rotation procedure, rotation results are obtained for both Phase 1 and Phase 2. A stitching process is then performed to stitch the two phase lines together. The basic steps in the stitching process are as follows:

  • ix. Shift Phase 1 in y-direction and stitch to Phase 2: As shown in Fig. 3, Phase 1 extending from D1” to D2” (marked by the red line) is totally shifted in the y-direction in order to stitch the pixel of D3′. Finally, the unwrapped phase composed of Phase 1 and Phase 2 is obtained. Note that the vector D1''D2'' is unchanged before and after Phase 1 is totally shifted in the y-direction. Therefore, the angle θ''fix between vectors D1''D2'' and D3'D4' is also the angle in the unwrapped phase composed of Phase 1 and Phase 2. By contrast, the angle θfix is between vectors D1D2 and D3D4 in the wrapped phase. The discussion of the angle values of θ''fix and θfix will be presented in the subsection 4.5.
  • x. Finish stitching process and start new first relative rotation procedure: Fig. 4
    Fig. 4 Starting position for new first relative rotation procedure based on stitching results shown in Fig. 3. The final cycle of the relative rotation stage is terminated when eliminating all of the 2π phase jumps. Totally, two cycles of the relative rotation stage are implemented in Fig. 1.
    shows the stitching result obtained in the previous step for Phase 1 and Phase 2 (see the blue line). Note that the blue line can be regarded as the 2D unwrapping result. According to the definition of Phase 1 provided in the subsection 2.2.1, the blue line can also be regarded as a new Phase 1. Thus, the first relative rotation procedure can be repeated using new Phase 1 and new Phase 2 extending from new pixels D3 to D4. Note that in new Phase 1, pixels D1 and D2 are taken from pixels D3′ and D4’, respectively. Furthermore, the position and phase value of the new center position, C1, are computed from Eq. (5) and Eq. (6), respectively, based on these new values of D1 and D2.
    Fig. 4Starting position for new first relative rotation procedure based on stitching results shown in Fig. 3. The final cycle of the relative rotation stage is terminated when eliminating all of the 2π phase jumps. Totally, two cycles of the relative rotation stage are implemented in Fig. 1.

3. Effect of proposed rotation algorithm on noise errors, hole errors and phase shifting errors

Figures 5(a)
Fig. 5 (a). Noise and holes are located outside of phase jump region. (b) Noise and holes straddle phase jump region. (c) Phase jump contains shifting error.
5(c) show three typical problems encountered when performing phase unwrapping. Note that the yellow and green triangles represent noise and holes in the wrapped phase map, respectively. Note also that D2 and D3 denote the 2π-high and 0-low positions of the phase jump, respectively. Figure 5(a) shows the situation in which the noise and holes are located outside of the phase jump region. Figure 5(b) shows the case where the noise and holes straddle the two phase jump positions. Finally, Fig. 5(c) shows the situation where the phase jump contains a shifting error. In typical 2π phase shifting algorithms, all three types of error are accumulated pixel-by-pixel along the unwrapping path, and thus the quality of the unwrapping results is seriously degraded.

3.1 Characteristic of angle θfix given above errors

As shown in Figs. 5(a), 5(b) and 5(c), the vector D1D2 obtained from pixels D1 and D2 is indicated by the solid line, while the vector D3D4 obtained from pixels D3 and D4 is indicated by the dashed line. Furthermore, the large arrows indicate the unwrapping path used in a typical 2π phase shifting algorithm. In Figs. 5(a) and 5(b), the arrow encounters the yellow triangles (i.e., noisy pixels), and thus the phase unwrapping process fails. However, given the hypothetical assumption that the unwrapping algorithm does not fail, the unwrapped phase in the two cases is marked by the red line. Similarly, in Fig. 5(c), the arrow passes through a shifting error which exists at D3. And thus the unwrapped phase also contains a shifting error since 2π phase shifting algorithm are incapable of removing this error during the unwrapping process (see the subsection 4.3). It is noted that the unwrapping process preserves the orientation of vector D3D4. In other words, the direction of vector D3D4 after the unwrapping process (brown-solid arrow) is parallel to that of vector D3D4 prior to the unwrapping process (brown-dashed arrow). In Figs. 5(a), 5(b) and 5(c), vectors D1D2 and D3D4 are separated by an angle θfix. Importantly, the vectors can be shifted to any position in the phase map without changing their direction in space. In other words, the yellow and green triangles (i.e. the noise or holes) do not change the direction of vectors D1D2 and D3D4 in each figure. As a result, angle θfix remains the same before and after phase unwrapping, respectively, in all three figures.

3.2 Advantage of vector C1C2 in proposed rotation algorithm

As discussed above, the reconstruction results obtained using unwrapping algorithms based on shifting 2π phase jumps invariably contain phase shifting errors. However, as described in the following, this problem is successfully resolved by the rotation algorithm proposed in this study. Recall that in Fig. 1, vector C1C2 is the central vector of Phase 1 and Phase 2. Importantly, this vector is not directly influenced by the shifting error since the evaluation of C1 and C2 in Eq. (5) and Eq. (6) does not include the shifting error. In the first relative rotation procedure, Phase 1 and Phase 2 are rotated in the counterclockwise or clockwise directions toward vector C1C2 and are then stitched together to form a single line with no shifting error. Importantly, C1 and C2 are recalculated at the beginning of each relative rotation cycle. Therefore, vector C1C2 reduces the shifting error more effectively than traditional algorithms based on shifting 2π phase jumps.

4. Simulation results

The unwrapping performance of the proposed rotation algorithm was evaluated by performing a series of MATLAB simulations on a PC equipped with an AMD Athion 64 × 2 Dual Core Processor 4400 + 2.31GHz and 2 GB of RAM.

4.1 2D non-noisy linear case (demonstrating total process in section 2 and presenting case in Fig. 5(a))

4.1.1 Finding all of phase jumps

The raw wrapped phase by applying the Hariharan algorithm [32

32. K. Liu, Y. C. Wang, D. L. Lau, Q. Hao, and L. G. Hassebrook, “Dual-frequency pattern scheme for high-speed 3-D shape measurement,” Opt. Express 18(5), 5229–5244 (2010). [CrossRef] [PubMed]

34

34. P. Hariharan, B. F. Oreb, and T. Eiju, “Digital phase-shifting interferometry: a simple error-compensating phase calculation algorithm,” Appl. Opt. 26(13), 2504–2506 (1987). [CrossRef] [PubMed]

] was produced by using a self-written MATLAB program, which contained one shallow hole less than π, one deep hole greater than π, and two height discontinuities, as illustrated in Fig. 6(a)
Fig. 6 (a) Detection result obtained using the detection scheme of Eq. (2) with σΑ = 2.8. (b) Detection result obtained using the additional phase jump detection scheme of Eq. (4).
. Note that this subsection not only demonstrated the total process in the section 2, but also presented the case in Fig. 5(a) where the wrapped phase contained holes which did not straddle a phase jump.

First, the detection scheme of Eq. (2) with σΑ=2.8 detects the phase jump positions (see the subsection 2.1.1) in Fig. 6(a), and the results are marked as two green symbols. For example, the first green symbol is located at the pixel row 80 (i=80), and the corresponding phase values yield ϕ(i,1)=ϕ(80,1)=6.258 and ϕ(i+1,1)=ϕ(81,1)=0.0643. Substituting ϕ(80,1)=6.258 and ϕ(81,1)=0.0643 into Eq. (2) gives
S1(80,1)=[-1.4351]+[0.5438]=1+1=0S2(80,1)=[-0.5438]+[1.4351]=1+1=0S3(80,1)=[-0.5438]+[0.5438]=1+1=0S4(80,1)=[-1.4351]+[1.4351]=1+1=0
(7)
From Eq. (7), the detection scheme defined the position of (80, 1) as a phase jump pixel. Then, the additional phase jump detection scheme of Eq. (4) detects the corresponding phase jumps missed by Eq. (2) (see the subsection 2.1.2). From Eq. (4) and Eq. (7), the first term in S1(80,1) yields [-1.4351]=1. The value of −1 represents that pixel ϕ(81,1) is located at the corresponding 0-low-position pixel. Therefore, the pixel of (81, 1) is chosen for another phase jump pixel, marked as the red symbol in Fig. 6(b). As shown in Fig. 6(b), two red symbols are obtained using Eq. (4). For the calculation process of the rotation algorithm, the first pixel of (1, 1) is always marked as the red symbol and the last pixel of (209, 1) is always marked as the green symbol. Finally, the relative rotation procedure starts from Fig. 6(b).

4.1.2 Performing relative rotation scheme

The first relative rotation from Step (i) to Step (vi) is performed, which is introduced in the subsection 2.2.1. Phase 1 (from D1 to D2) and Phase 2 (from D3 to D4) can be obtained since Fig. 6(b) gives the known pixels D1 (1, 0.09), D2 (80, 6.258), D3 (81, 0.0643), and D4 (150, 6.274) (row, phase) as shown in Fig. 7(a)
Fig. 7 (a) Results of first relative rotation procedure (Steps (i) to (vi)), indicated by black-dashed line and black-solid line. (b) Results of second relative rotation procedure (Steps (vii) to (viii)), indicated by green line.
. In Step (i) (see the subsection 2.2.1), center pixels C1 and C2 are determined via Eqs. (5) and (6). The calculation result yields C1 (53.725, 4.8352) and C2 (127.0933, 4.2127) (row, phase) which are marked as two blue points in Fig. 7(a). In Step (ii), vectors of D1D2, D3D4 and C1C2 can be directly determined using the known positions D1, D2, D3, D4, C1, and C2. Then, in Steps (iii), the angle of θfix between vectors of D1D2 and D3D4 can be determined and the calculation result is 0.68°. In Step (iv), the known vectors of D1D2 and C1C2 yields the angle θ1 = 4.95°, and the known vectors of D3D4 and C1C2 yields the angle θ2 = 5.63°. In Step (v), Phase 2 and Phase 1 are rotated through angles of θ2+( = + 5.63°) and θ1+( = + 4.95°) about C2 and C1 toward vector C1C2, respectively (Note that the clockwise direction is positive). In Step (vi), the first relative rotation procedure terminates; yielding the black-dashed line (Phase 1) and black-solid line (Phase 2) shown in Fig. 7(a).

The second relative rotation procedure starts after the first relative rotation procedure is finished that pixels D1’, D2’, D3′, and D4’ yield (0.7888, 5.284), (81.01, 3.673), (80.82, 4.605), and (150.1, 4.018) (row, phase). In Step (vii), the angle θ'fix between the two known vectors D1'D2' andD3'D4' can yield θ'fix = 0°. Additionally, the tangent function of unit vector of D1D2 is equal to 0.0782 and the tangent function of unit vector of D3D4 is equal to 0.0902. According to Step (viii), since the tangent function of unit vector of D1D2 is less than that of unit vector of D3D4 at D2’, the corresponding equation yields θ''fix=θfixθ'fix. Substituting θfix = 0.68° and θ'fix = 0° into θ''fix=θfixθ'fix gives θ''fix = 0.68°-0° = + 0.68°. And thus, as shown in Fig. 7(b), the black-dashed line needs to be clockwise rotated by an angle θ''fix+( = + 0.68°) about the fixed center C1 and the rotation result is marked as the green line.

Figure 8(a)
Fig. 8 (a) Results of stitching process (Steps (ix) to (x)), indicated by red line. (b) Starting point for new relative rotation cycle using new Phase 1 (black-dashed line) and new Phase 2 (black-solid line).
presents the stitching results (Steps (ix) and (x)), in which the green line (i.e., Phase 1) in Fig. 7(b) is shifted in the y-direction to a new position (indicated by the red line) and stitched to Phase 2. The relative rotation cycle is then repeated using the red line and black-solid line as new Phase 1 (from the pixel row 1 through new D1 (80.81, 4.221) toward new D2 (150.1, 4.413)). As shown in Fig. 8(b), two blue lines represent the new Phase 1 (as presented above) and the new Phase 2 (from new D3 (151, 0.0812) to new D4 (209, 6.244), respectively. Similarly, in Step (i), Eq. (5) and Eq. (6) give the new center pixels C1 (114.6391, 4.3134) and C2 (189.9587, 4.522). From Step (ii) to Step (iv), the known six pixels of D1, D2, D3, D4, C1, and C2 are used to determine the values of D1D2, D3D4, C1C2, θfix, θ1, and θ2. The calculation results present that θfix = 0.92°, θ1 = −0.64°, θ2 = 5.91°, as shown in Fig. 8(b). In the following Step (v), since angles of θ1 and θ2 need to rotate toward the vector of C1C2, the new Phase 2 is rotated through θ2+( = + 5.91°) in a clockwise direction and the new Phase 1 is rotated through θ1( = −0.64°) in a counter-clockwise direction, as shown in Fig. 8(b). After the first relative rotation procedure terminates, one black-dashed line and one black-solid line are presented in Fig. 8(b).

Subsequently, the second relative rotation procedure is performed. In Step (vii), the two known vectors D1'D2' and D3'D4' can determine that θ'fix = 0°. In Step (viii), the tangent function of unit vector of D1D2 ( = 0.0902) is less than that of unit vector of D3D4 ( = 0.1066). And thus, substituting θfix = 0.92° and θ'fix = 0° into θ''fix=θfixθ'fix gives = + 0.92°. After the black-dashed line is rotated through θ''fix+( = 0.92°) in a clockwise direction, the green line is obtained and shown in Fig. 9(a)
Fig. 9 (a) Results of second relative rotation procedure (Steps (vii) to (viii)) in new relative rotation cycle, indicated by green line. (b) Stitching results (Steps (ix) to (x)) in new relative rotation cycle, indicated by red line and black line.
. Finally, the stitching process is performed. Figure 9(b) shows the results of Step (ix), in which the green line is shifted in the y-direction to the position indicated by the red line and stitched to Phase 2. Note that Step (x) is not performed since the unwrapped phase is obtained.

Figures 10(a)
Fig. 10 (a) Phase unwrapping results obtained using MACY algorithm. (b) Phase unwrapping results obtained using CA algorithm.
and 10(b) present the phase unwrapping results obtained using the MACY algorithm and the CA algorithm, respectively. Note that in the two figures, the blue line represents the raw wrapped phase, while the black line indicates the unwrapping result. It is seen that both unwrapping algorithms convert a concave-deep hole into a convex-shallow hole. By contrast, the proposed rotation algorithm retains the shallow and deep holes in the reconstruction results (see Fig. 9(b)). In other words, the proposed algorithm provides a more effective unwrapping solution for the case where the wrapped phase contains holes which do not straddle a phase jump (i.e., the condition shown in Fig. 5(a)). The runtime for MACY was found to be 0.9 seconds, and the runtime for CA was found to be 4.3 seconds. Compared to MACY and CA, the runtime for the proposed rotation algorithm was found to be 1.5 seconds, which was between MACY and CA.

4.2 2D noisy linear case (containing speckle noise, residual noise, and holes, and resenting case in Figs. 5(a) and 5(b))

The original five interferograms converting into the wrapped phase in Fig. 6(a) were used in this subsection again. The noise and holes were added to these original interferograms. These five interferograms were then merged to form a single raw wrapped phase (see Fig. 11(a)
Fig. 11 Detection result obtained using the detection scheme of Eq. (2) (a) with σΑ = 1 and (b) with σΑ = 2.8.
, in which the noise and holes were labeled as “Shallow hole”, “Deep hole”, “Residual noise” and “Speckle noise (distributed in the whole phase)”). Speckle noise was a random noise mixed with the interference pattern within the map, and was generated using the “imnoise” function in MATLAB with an intensity parameter setting of 0.3 (i.e., “imnoise (each of five interferograms, 'speckle', 0.3)”). In practical interferometry systems, the interferograms contained residual noise due to the effects of environmental or experimental contamination. In the present simulations, this residual noise was reproduced using the MATLAB “imnoise” function with salt and pepper noise and an intensity parameter of 0.25, written as “imnoise (each of five interferograms, 'salt & pepper', 0.25”). Since one residual noise was located outside of phase jump region and one residual noise straddled phase jump region, this subsection presented the cases as shown in Figs. 5(a) and 5(b).

As shown in Fig. 11(a), the green symbols present the phase jump pixels and the black symbols present the bad pixels (or the noise), which are detected by the detection scheme of Eq. (2) with σΑ = 1. From an inspection of Fig. 11(a), the phase jump pixels are not correct and the bad pixels are too less. As shown in Fig. 11(b), when a threshold parameter value of σΑ is equal to 2.8, the two green symbols are located at the correct phase jump positions and the black symbols are distributed in the noise positions. Therefore, the threshold parameter was set to σΑ=2.8 in this subsection. Figure 12(a)
Fig. 12 (a) Detection result obtained using the additional phase jump detection scheme of Eq. (4). (b) Unwrapped Result obtained from relative rotation process.
presents that the corresponding phase jump pixels are detected by Eq. (4), and are marked as the red symbols. An inspection of Fig. 12(a) reveals that the serious noise does not cause Eq. (4) to produce any detection error.

Subsequently, the wrapped phase in Fig. 12(a) is performed by the relative rotation algorithm. As a result, the unwrapped phase in Fig. 12(b) retains one shallow hole, one deep hole, one residual noise (outside of phase jump region), one residual noise (straddled phase jump region), and speckle noise. The runtime was found to be 1.6 seconds. By contrast, the wrapped phase in Fig. 11(a) is also performed by the MACY algorithm and the CA algorithm, and the unwrapped results are shown in Figs. 13(a)
Fig. 13 (a) Phase unwrapping results obtained using MACY algorithm. (b) Phase unwrapping results obtained using CA algorithm.
and 13(b), respectively. Obviously, the unwrapping process fails since the unwrapping path encounters two of the residual noise and one deep hole. Overall, the proposed rotation algorithm can successfully retain all of the noise and holes.

4.3 2D filtered linear case (presenting shifting error in Fig. 5(c))

To demonstrate the shifting error produced by a filtering algorithm, Filter A [11

11. J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express 20(10), 10896–10920 (2012). [CrossRef] [PubMed]

] was applied 10 times to the raw wrapped phase shown in Fig. 6(a). Filter A comprises the detection scheme proposed in [10

10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

] and the adaptive median filter proposed in [16

16. A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36(9), 2466–2472 (1997). [CrossRef]

]. Briefly, the noise is removed (while simultaneously preserving most of the phase jumps) using Filter A. After filtering 10 times, a significant shifting error is produced as the result of a smearing of the phase jumps in the wrapped phase. Figure 14
Fig. 14 Filtered wrapped phase following 10 times by Filter A [11].
shows the filtered phase (red line) and the original wrapped phase (blue-dashed line) presented in Fig. 6(a). Comparing the two phases, it can be seen that Filter A not only destroys two holes, but also smears all phase jumps, such as the filtered-high phase jump position (80, 6.168) and filtered-low phase jump position (81, 0.1543). In other words, a phase shifting error is produced. Note that the difference in the filtered phase values of pixels (80, 6.168) and (81, 0.1543), respectively, is equal to 6.01 rather than 2π because the filter smears the 2π phase jump. The shifting error is equal to −0.27 (i.e., 6.01-2π). The filtered phase values were processed using MACY, CA and the relative rotation procedure, respectively. Although the filtered-deep hole causes the MACY and CA to fail, no accumulated error is produced to affect the other phase values in the whole unwrapped phase. Therefore, the unwrapped phase by performing MACY or CA is acceptable except the filtered-deep hole. The corresponding phase differences between the pixel row 80 and the pixel row 81 are summarized in Table 1

Table 1. Phase difference values between pixels (80, 6.168) and (81, 0.1543) (row, phase) in three different unwrapping schemes.

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. It is seen that the MACY and CA algorithms yield a phase difference of −0.27, which is equal to the shifting error. In other words, MACY and CA simply retain the shifting error during the unwrapping process. By contrast, the phase difference between the two pixels following the relative rotation procedure is equal to just 0.05. In other words, the proposed relative rotation algorithm reduces the shifting error. Therefore, the problem shown in Fig. 5(c) is resolved. The runtime of the relative rotation procedure was found to be 1.2 seconds.

4.4 2D non-noisy non-linear case (presenting sine-wave case)

The raw wrapped phase was also obtained by applying the Hariharan algorithm [33

33. K. Creath, “Phase-shifting speckle interferometry,” Appl. Opt. 24(18), 3053–3058 (1985). [CrossRef] [PubMed]

,34

34. P. Hariharan, B. F. Oreb, and T. Eiju, “Digital phase-shifting interferometry: a simple error-compensating phase calculation algorithm,” Appl. Opt. 26(13), 2504–2506 (1987). [CrossRef] [PubMed]

] to the five interferograms. Again, the wrapped phase was generated by using a self-written MATLAB program, which contained the sine-wave line shown in Fig. 15(a)
Fig. 15 (a) Detection result obtained using the detection scheme of Eq. (2) with σΑ = 2.8. (b) Detection result obtained using the additional phase jump detection scheme of Eq. (4). (c) Unwrapped result obtained from relative rotation process.
. In Figs. 6(a) and 11(b), the detected phase jump pixels marked as the green symbols are always located at the 2π-high-positions using the detection scheme of Eq. (2). By contrast, as shown in Fig. 15(a), after performing the detection scheme with a threshold parameter setting of σΑ = 2.8, the detected phase jump pixels marked as the green symbols are located at not only the 2π-high-positions but also the 0-low-positions. Moreover, the additional phase jump detection scheme of Eq. (4) can detect the corresponding 2π-high-positions or the corresponding 0-low-positions marked as the red symbols in Fig. 15(b). Then, the wrapped phase shown in Fig. 15(b) is performed by the relative rotation procedure, and the unwrapped result is shown in Fig. 15(c). An inspection of Fig. 15(c) reveals that the sine-wave line is successfully reconstructed. The runtime was found to be 1.1 seconds. Collectively, the detection scheme of Eq. (2) and the additional phase jump detection scheme of Eq. (4) can detect all of phase jump pixels (i.e. 0 and 2π positions), and the relative rotation procedure can be applied to not only the linear cases but also the non-linear (i.e. sine-wave) cases.

4.5 Discussion of rotation angles

Table 2

Table 2. The corresponding angles in Cycle 2 in the relative rotation procedure

table-icon
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summarizes the angles, θfix, θ1, θ2, θ'fix, and θ''fix used on the one repeat time (i.e. Cycle 2) performing by the relative rotation procedure introduced in the subsections 4.1.2, 4.2, 4.3, and 4.4, respectively. The positive angle values are defined as the clockwise rotation direction, while the negative angle values are defined as the counter-clockwise rotation direction. The wrapped phases shown in Figs. 6(a), 11(a), and 14 are regarded as the same shape, which contain one shallow hole, one deep hole, and two height discontinuities. The wrapped phase shown in Fig. 11(a) is the noisy wrapped phase obtained from the original non-noisy phase shown in Fig. 6(a). Nevertheless, the wrapped phase shown in Fig. 14 is the filtered wrapped phase by Filter A [11

11. J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express 20(10), 10896–10920 (2012). [CrossRef] [PubMed]

] filtering the original non-noisy wrapped phase shown in Fig. 6(a). It is seen that the noise causes the original angle value of θfix (0.92°) in original non-noisy phase to reduce with the value of 0.83° in noisy phase. By contrast, the angle of θfix is equal to 0.89° in filtered wrapped phase, which is just slight less than the original value of 0.92° in original non-noisy phase. Therefore, compared to the filter, the noise more seriously influences vectors D1D2 and D3D4 (or these four pixels positions).

An inspection of the angles of θ1 and θ2 in Table 2 reveals that the angle θ1 is negative (i.e. counter-clockwise) in original non-noisy phase and noisy phase, while the angle θ1 is positive (i.e. clockwise) in filtered phase. As shown in Fig. 14, it is seen that two original holes marked as the blue-dashed line are seriously destroyed by performing Filter A, and thus the moved positions of two holes marked as the red-solid lines cause the original positions of centers C1 and C2 to yield new positions. The new positions of pixels C1 and C2 directly influence the angles of θ1 and θ2. Therefore, the angle of θ1 in filtered phase is greater than angles of θ1 in original non-noisy phase and noisy phase. In addition, in Table 2, the angles of θ1 and θ2 in sine-wave phase are greater than those angles in other three phases. It is seen that those three phases just contain the detected green symbols located at the 2π-high-positions using the detection scheme, while the sine-wave phase contains the detected green symbols located at not only 2π-high-positions but also the 0-low-positions. Accordingly, in the sine-wave case, the relative rotation procedure needs to rotate through the greater angles of θ1 and θ2.

Finally, as shown in Table 2, four corresponding angles of θ'fix are equal to 0. When angle of θ'fix is equal to 0, it means both of vectors D1'D2' and D3'D4' are parallel to the vector C1C2. And thus, the absolute value of θ''fix is equal to the valve of θfix, which is obtained from the equation θ''fix=θ'fixθfix or θ''fix=θfixθ'fix. According to Step (ix), the angle of θfix represents the angle in wrapped phase, while the angle of θ''fix represents the angle in unwrapped phase. In Table 2, comparing the angles of θfix and θ''fix, both of the corresponding absolute values are same, which are presented as 0.92°, 0.83°, 0.89°, 5.67°. Therefore, the proposed rotation algorithm based on operating the angle of θfix is reasonable for the theories and is more robust than the most of existing 2π phase shifting unwrapping algorithms.

4.6 Basic process in 3D wrapped phase map

The influence of noise or the filters to the angle of θfix is described in the subsection 4.5. Accordingly, in the 3D wrapped phase map (i.e. (row, column, phase)), different 2D wrapped phase lines (i.e. (row, phase)) contain different noise distribution, thus the corresponding angles of θfix are different line-by-line. Obviously, the 3D image reconstruction cannot be obtained by just performing the relative rotation algorithm line-by-line. However, in order to reconstruct the 3D unwrapped phase map by using the proposed rotation algorithm, the absolute rotation algorithm is proposed to operate the 2D unwrapped phase line-by-line, which will be introduced in the future. Briefly, after the 3D wrapped phase map is detected by the detection scheme of Eq. (1), the noise map and phase jump map are produced. Comparing the noise map and phase jump map, a suitable parameter threshold setting of σΑ can be decided. Then, the additional phase jump detection scheme of Eq. (3) is used to detect the corresponding phase jump pixels missed by Eq. (1). After all of the phase jumps are detected by Eq. (1) and Eq. (3), the relative rotation procedure is performed line-by-line. Finally, the absolute rotation procedure maps the unwrapped phase of each 2D coordinate in the relative rotation reconstruction result to the corresponding 3D coordinate, thereby producing a 3D unwrapped phase map. Moreover, the absolute rotation procedure can make the 3D unwrapped phase map to be effectively filtered by Filter B [11

11. J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express 20(10), 10896–10920 (2012). [CrossRef] [PubMed]

] if required. Filter B simply replaces the pixels in the noise map constructed by the detection scheme in [11

11. J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express 20(10), 10896–10920 (2012). [CrossRef] [PubMed]

] with the median phase value of an N x N mask centered on the noisy pixel.

5. Experimental results

The effectiveness of the proposed unwrapping methodology was demonstrated experimentally by performing a series of interferometry trials using various representative samples. The trials were conducted using a Mirau interferometer configuration comprising a microscope (OLYMPUS BH2-UMA) fitted with a 20XDL objective lens (NA 0.4, WD 4.7, Nikon JAPAN), a CCD camera (JAI CV-A11, monochrome progressive scan 1/3 inch), and a lens (6-60 mm, F:1.6, Kenko, JAPAN). In every case, the sample was illuminated by a white-light LED (LP-1201H-3-IO, EXLITE) with a filter central wavelength of 633 nm or a 632.8 nm He-Ne laser (MODEL 1135P, JDS Uniphase). For each sample, five interferograms were obtained. The interferograms were converted into a single raw wrapped phase map using the Hariharan algorithm [33

33. K. Creath, “Phase-shifting speckle interferometry,” Appl. Opt. 24(18), 3053–3058 (1985). [CrossRef] [PubMed]

,34

34. P. Hariharan, B. F. Oreb, and T. Eiju, “Digital phase-shifting interferometry: a simple error-compensating phase calculation algorithm,” Appl. Opt. 26(13), 2504–2506 (1987). [CrossRef] [PubMed]

]. The spatial resolution of the wrapped phase map was equal to approximately 0.42 µm per length between two pixels in both the row and column directions.

5.1 Retention of holes in proposed relative rotation algorithm

The ability of the proposed relative rotation algorithm to preserve holes in the sample surface in the reconstruction results was evaluated using a Si sample containing two holes. Figure 16(a)
Fig. 16 (a) Raw wrapped phase of Si sample using white-light source. (b) Unwrapped result obtained from relative rotation process.
presents the corresponding raw wrapped phase with dimensions of 200 × 1 (row × column). Moreover, the green and red symbols shown in Fig. 16(a) are the detected results using Eq. (2) and Eq. (4) with a detection threshold parameter setting of σΑ = 2.75. It is noted that the holes are located away from the phase jumps (i.e., as in the condition illustrated in Fig. 5(a)). Figure 16(b) shows the reconstruction results obtained from the relative rotation procedure, marked as the black-solid line. Obviously, Fig. 16(b) shows that the rotation algorithm successfully preserves the two holes in the sample surface when unwrapping the wrapped phase. Thus, in this particular example, the unwrapping process should terminate following completion of the relative rotation algorithm without the filtering algorithm. The runtime was found to be 1.5 seconds.

5.2 Retention of noise in proposed relative rotation algorithm

The robustness of the proposed rotation algorithm toward noise was evaluated using a rough Si sample. Figure 17(a)
Fig. 17 (a) Raw wrapped phase of Si sample using laser source. (b) Unwrapped result obtained from relative rotation process.
shows that the raw wrapped phase contains the residual noise as a result of the rough sample surface and the speckle noise as a result of the laser source (with dimensions of 337 × 1 (row × column)). The green and red symbols are obtained using a detection threshold parameter setting of σΑ = 2.65. Figure 17(b) shows the reconstruction results obtained from the relative rotation procedure. It can be seen that the residual noise and speckle noise are retained and those noises can be removed by the filtering process if required. The runtime by using the relative rotation procedure was found to be 1.2 seconds.

5.3 Robustness of proposed relative and absolute rotation algorithms toward noise

This subsection demonstrates that the absolute rotation algorithm can successfully operate all of the 2D unwrapped phase lines using the relative rotation algorithm, and can reconstruct the 3D unwrapped phase map. The robustness of the proposed rotation algorithm toward noise was evaluated using a rough TaSiN step height sample with a single height discontinuity. Figure 18(a)
Fig. 18 (a) Raw wrapped phase map of step height sample with rough surface. (b) Noise map given σΑ = 2.75.
shows the raw wrapped phase map (with dimensions of 265 × 265 (row × column)), while Fig. 18(b) shows the noise map obtained using a detection threshold parameter setting of σΑ = 2.75. The results presented in Fig. 18(b) show that the wrapped phase map contains both residual noise as a result of the rough sample surface and obvious noise at a single height discontinuity. Figure 19(a)
Fig. 19 Results of absolute rotation procedure. (a) Without Filter B. (b) With Filter B.
shows the reconstruction results obtained from the absolute rotation procedure. The runtime was found to be 35 seconds. It can be seen that the residual noise and noise at a single height discontinuity are retained, but are regarded as the adverse phase distributions here. Thus, Filter B with the 9 × 9 mask and the detection scheme σΒ = 0. 5 is used 2 times to remove the residual noise and the noise at a single height discontinuity in Fig. 19(a). Figure 19(b) shows the filtered unwrapped phase map. The filtering runtime was found to be 32 seconds. It is seen that the filter removes the residual noise and noise at single height discontinuity while preserving the height discontinuity feature. The filtered reconstruction results are qualitatively clearer than the original (unfiltered) results. Thus, in this particular case, the unwrapping procedure should terminate after the filtering process.

6. Conclusions

This study has proposed a novel rotation algorithm for phase unwrapping applications. The proposed algorithm comprises two stages, namely a relative phase rotation stage and an absolute phase rotation stage. The relative phase rotation stage yields an initial reconstruction result using the detected phase jump pixels by Eqs. (1)(4). The absolute phase rotation stage refines the initial reconstruction result such that it is to further processing using a noise reduction filter, which will be introduced in the future. In contrast to traditional unwrapping algorithms, the raw wrapped phase map is not filtered prior to the unwrapping process. That is, the reconstruction results retain the noise and hole errors in the unwrapped phase map. However, a filter [11

11. J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express 20(10), 10896–10920 (2012). [CrossRef] [PubMed]

] can be optionally applied to the reconstruction results to remove these errors if required. Since the filtering process is directly applied to the unwrapped phase rather than the raw wrapped phase, the phase shifting error induced in traditional 2π phase jump unwrapping schemes is avoided.

The effectiveness of the proposed unwrapping methodology has been demonstrated numerically given various common reconstruction problems. In general, the 2D reconstruction results have shown that while the proposed algorithm retains the noise and hole errors in the reconstruction results, these errors do not prevent the algorithm to obtain a reliable solution. By contrast, in the MACY and CA algorithms, the presence of noise and holes in the unwrapped phase map prevents the reconstruction process to obtain the reliable solutions. Furthermore, since a filtering process is performed prior to the unwrapping process, both algorithms generate a phase shifting error.

The 2D experimental results have confirmed the ability of the proposed relative rotation algorithm to reconstruct holes, speckle noise, and residual noise in the sample surface which has been demonstrated using a Si sample. Moreover, one 3D experimental result has confirmed the robustness of the absolute rotation algorithm toward residual noise and noise at a single height discontinuity which has been demonstrated using a rough TaSiN sample. It has been shown that the use of an additional filtering process is beneficial in improving the quality of the 3D reconstruction result of TaSiN sample.

In conclusion, from the angle value of θ''fix equal to that of θfix, the rotation theories based on operating the angle of θfix are reasonable and are more effective than the most of existing 2π phase shifting unwrapping algorithms, which is not only robust toward the presence of noise and hole errors in the wrapped phase, but also avoids the phase shifting error induced in traditional 2π phase jump unwrapping schemes such as MACY and CA. For the simulation and experimental examples considered in this study, a successful (i.e., reliable) reconstruction result was obtained within 1~2 s for 2D coordinate and within 35~67 s for 3D coordinate when using a general PC (AMD Athion 64 × 2 Dual Core Processor 4400 + 2.31GHz and 2 GB of RAM). An inspection of 2D coordinate reveals that the runtime for the proposed relative rotation algorithm is faster than that for CA and slower than that for MACY.

Acknowledgments

The authors gratefully acknowledge the financial support provided to this study by the National Science Council of Taiwan under grant NSC 98-2221-E-006-053-MY3.

References and links

1.

B. F. Pouet and S. Krishnaswamy, “Technique for the removal of speckle phase in electronic speckle interferometry,” Opt. Lett. 20(3), 318–320 (1995). [CrossRef] [PubMed]

2.

I. Moon and B. Javidi, “Three-dimensional speckle-noise reduction by using coherent integral imaging,” Opt. Lett. 34(8), 1246–1248 (2009). [CrossRef] [PubMed]

3.

M. J. Huang and J. K. Liou, “Retrieving ESPI map of discontinuous objects via a novel phase unwrapping algorithm,” Strain 44(3), 239–247 (2008). [CrossRef]

4.

R. Yamaki and A. Hirose, “Singularity-spreading phase unwrapping,” IEEE Trans. Geosci. Rem. Sens. 45(10), 3240–3251 (2007). [CrossRef]

5.

E. H. Kim, J. Hahn, H. Kim, and B. Lee, “Profilometry without phase unwrapping using multi-frequency and four-step phase-shift sinusoidal fringe projection,” Opt. Express 17(10), 7818–7830 (2009). [CrossRef] [PubMed]

6.

W. H. Su, K. Shi, Z. Liu, B. Wang, K. Reichard, and S. Yin, “A large-depth-of-field projected fringe profilometry using supercontinuum light illumination,” Opt. Express 13(3), 1025–1032 (2005). [CrossRef] [PubMed]

7.

P. Potuluri, M. Fetterman, and D. Brady, “High depth of field microscopic imaging using an interferometric camera,” Opt. Express 8(11), 624–630 (2001). [CrossRef] [PubMed]

8.

H. O. Saldner and J. M. Huntley, “Temporal phase unwrapping: application to surface profiling of discontinuous objects,” Appl. Opt. 36(13), 2770–2775 (1997). [CrossRef] [PubMed]

9.

A. Wada, M. Kato, and Y. Ishii, “Large step-height measurements using multiple-wavelength holographic interferometry with tunable laser diodes,” J. Opt. Soc. Am. A 25(12), 3013–3020 (2008). [CrossRef] [PubMed]

10.

J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express 19(4), 3086–3105 (2011). [CrossRef] [PubMed]

11.

J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express 20(10), 10896–10920 (2012). [CrossRef] [PubMed]

12.

J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49(11), 2143–2150 (2010). [CrossRef] [PubMed]

13.

H. A. Aebischer and S. Waldner, “A simple and effective method for filtering speckle-interferometric phase fringe patterns,” Opt. Commun. 162(4–6), 205–210 (1999). [CrossRef]

14.

H. O. Saldner and J. M. Huntley, “Temporal phase unwrapping: application to surface profiling of discontinuous objects,” Appl. Opt. 36(13), 2770–2775 (1997). [CrossRef] [PubMed]

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S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express 15(13), 8059–8064 (2007). [CrossRef] [PubMed]

16.

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36(9), 2466–2472 (1997). [CrossRef]

17.

J. M. Huntley and H. Saldner, “Temporal phase-unwrapping algorithm for automated interferogram analysis,” Appl. Opt. 32(17), 3047–3052 (1993). [CrossRef] [PubMed]

18.

D. S. Mehta, S. K. Dubey, M. M. Hossain, and C. Shakher, “Simple multifrequency and phase-shifting fringe-projection system based on two-wavelength lateral shearing interferometry for three-dimensional profilometry,” Appl. Opt. 44(35), 7515–7521 (2005). [CrossRef] [PubMed]

19.

S. Zhang, X. L. Li, and S. T. Yau, “Multilevel quality-guided phase unwrapping algorithm for real-time three-dimensional shape reconstruction,” Appl. Opt. 46(1), 50–57 (2007). [CrossRef] [PubMed]

20.

W. W. Macy Jr., “Two-dimensional fringe-pattern analysis,” Appl. Opt. 22(23), 3898–3901 (1983). [CrossRef] [PubMed]

21.

D. C. Ghiglia, G. Mastin, and L. A. Romero, “Cellular-automata method for phase unwrapping,” J. Opt. Soc. Am. A 4(1), 267–280 (1987). [CrossRef]

22.

A. Spik and D. W. Robinson, “Investigation of the cellular automata method for phase unwrapping and its implementation on an array processor,” Opt. Lasers Eng. 14(1), 25–37 (1991). [CrossRef]

23.

H. Y. Chang, C. W. Chen, C. K. Lee, and C. P. Hu, “The Tapestry Cellular Automata phase unwrapping algorithm for interferogram analysis,” Opt. Lasers Eng. 30(6), 487–502 (1998). [CrossRef]

24.

K. A. Stetson, J. Wahid, and P. Gauthier, “Noise-immune phase unwrapping by use of calculated wrap regions,” Appl. Opt. 36(20), 4830–4838 (1997). [CrossRef] [PubMed]

25.

R. Goldstein, H. Zebker, and C. Werner, “Satellite radar interferometry: two-dimensional phase unwrapping,” Radio Sci. 23(4), 713–720 (1988). [CrossRef]

26.

T. J. Flynn, “Two-dimensional phase unwrapping with minimum weighted discontinuity,” J. Opt. Soc. Am. A 14(10), 2692–2701 (1997). [CrossRef]

27.

M. A. Navarro, J. C. Estrada, M. Servin, J. A. Quiroga, and J. Vargas, “Fast two-dimensional simultaneous phase unwrapping and low-pass filtering,” Opt. Express 20(3), 2556–2561 (2012). [CrossRef] [PubMed]

28.

H. Cui, W. Liao, N. Dai, and X. Cheng, “Reliability-guided phase-unwrapping algorithm for the measurement of discontinuous three-dimensional objects,” Opt. Eng. 50(6), 063602 (2011). [CrossRef]

29.

J. C. Estrada, M. Servin, and J. Vargas, “2D simultaneous phase unwrapping and filtering: a review and comparison,” Opt. Eng. 50(8), 1026–1029 (2011).

30.

X. Xianming and P. Yiming, “Multi-baseline phase unwrapping algorithm based on the unscented Kalman filter,” IET Radar Sonar Nav. 5(3), 296–304 (2011). [CrossRef]

31.

J. J. Martinez-Espla, T. Martinez-Marin, and J. M. Lopez-Sanchez, “Using a Grid-Based Filter to Solve InSAR Phase Unwrapping,” IEEE Geosci. Remote Sens. 5(2), 147–151 (2008). [CrossRef]

32.

K. Liu, Y. C. Wang, D. L. Lau, Q. Hao, and L. G. Hassebrook, “Dual-frequency pattern scheme for high-speed 3-D shape measurement,” Opt. Express 18(5), 5229–5244 (2010). [CrossRef] [PubMed]

33.

K. Creath, “Phase-shifting speckle interferometry,” Appl. Opt. 24(18), 3053–3058 (1985). [CrossRef] [PubMed]

34.

P. Hariharan, B. F. Oreb, and T. Eiju, “Digital phase-shifting interferometry: a simple error-compensating phase calculation algorithm,” Appl. Opt. 26(13), 2504–2506 (1987). [CrossRef] [PubMed]

OCIS Codes
(100.6890) Image processing : Three-dimensional image processing
(100.5088) Image processing : Phase unwrapping

ToC Category:
Image Processing

History
Original Manuscript: March 9, 2012
Revised Manuscript: May 31, 2012
Manuscript Accepted: July 3, 2012
Published: July 11, 2012

Citation
Jing-Feng Weng and Yu-Lung Lo, "Novel rotation algorithm for phase unwrapping applications," Opt. Express 20, 16838-16860 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-15-16838


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References

  1. B. F. Pouet and S. Krishnaswamy, “Technique for the removal of speckle phase in electronic speckle interferometry,” Opt. Lett.20(3), 318–320 (1995). [CrossRef] [PubMed]
  2. I. Moon and B. Javidi, “Three-dimensional speckle-noise reduction by using coherent integral imaging,” Opt. Lett.34(8), 1246–1248 (2009). [CrossRef] [PubMed]
  3. M. J. Huang and J. K. Liou, “Retrieving ESPI map of discontinuous objects via a novel phase unwrapping algorithm,” Strain44(3), 239–247 (2008). [CrossRef]
  4. R. Yamaki and A. Hirose, “Singularity-spreading phase unwrapping,” IEEE Trans. Geosci. Rem. Sens.45(10), 3240–3251 (2007). [CrossRef]
  5. E. H. Kim, J. Hahn, H. Kim, and B. Lee, “Profilometry without phase unwrapping using multi-frequency and four-step phase-shift sinusoidal fringe projection,” Opt. Express17(10), 7818–7830 (2009). [CrossRef] [PubMed]
  6. W. H. Su, K. Shi, Z. Liu, B. Wang, K. Reichard, and S. Yin, “A large-depth-of-field projected fringe profilometry using supercontinuum light illumination,” Opt. Express13(3), 1025–1032 (2005). [CrossRef] [PubMed]
  7. P. Potuluri, M. Fetterman, and D. Brady, “High depth of field microscopic imaging using an interferometric camera,” Opt. Express8(11), 624–630 (2001). [CrossRef] [PubMed]
  8. H. O. Saldner and J. M. Huntley, “Temporal phase unwrapping: application to surface profiling of discontinuous objects,” Appl. Opt.36(13), 2770–2775 (1997). [CrossRef] [PubMed]
  9. A. Wada, M. Kato, and Y. Ishii, “Large step-height measurements using multiple-wavelength holographic interferometry with tunable laser diodes,” J. Opt. Soc. Am. A25(12), 3013–3020 (2008). [CrossRef] [PubMed]
  10. J. F. Weng and Y. L. Lo, “Robust detection scheme on noise and phase jump for phase maps of objects with height discontinuities--theory and experiment,” Opt. Express19(4), 3086–3105 (2011). [CrossRef] [PubMed]
  11. J. F. Weng and Y. L. Lo, “Integration of robust filters and phase unwrapping algorithms for image reconstruction of objects containing height discontinuities,” Opt. Express20(10), 10896–10920 (2012). [CrossRef] [PubMed]
  12. J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt.49(11), 2143–2150 (2010). [CrossRef] [PubMed]
  13. H. A. Aebischer and S. Waldner, “A simple and effective method for filtering speckle-interferometric phase fringe patterns,” Opt. Commun.162(4–6), 205–210 (1999). [CrossRef]
  14. H. O. Saldner and J. M. Huntley, “Temporal phase unwrapping: application to surface profiling of discontinuous objects,” Appl. Opt.36(13), 2770–2775 (1997). [CrossRef] [PubMed]
  15. S. Yuqing, “Robust phase unwrapping by spinning iteration,” Opt. Express15(13), 8059–8064 (2007). [CrossRef] [PubMed]
  16. A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng.36(9), 2466–2472 (1997). [CrossRef]
  17. J. M. Huntley and H. Saldner, “Temporal phase-unwrapping algorithm for automated interferogram analysis,” Appl. Opt.32(17), 3047–3052 (1993). [CrossRef] [PubMed]
  18. D. S. Mehta, S. K. Dubey, M. M. Hossain, and C. Shakher, “Simple multifrequency and phase-shifting fringe-projection system based on two-wavelength lateral shearing interferometry for three-dimensional profilometry,” Appl. Opt.44(35), 7515–7521 (2005). [CrossRef] [PubMed]
  19. S. Zhang, X. L. Li, and S. T. Yau, “Multilevel quality-guided phase unwrapping algorithm for real-time three-dimensional shape reconstruction,” Appl. Opt.46(1), 50–57 (2007). [CrossRef] [PubMed]
  20. W. W. Macy., “Two-dimensional fringe-pattern analysis,” Appl. Opt.22(23), 3898–3901 (1983). [CrossRef] [PubMed]
  21. D. C. Ghiglia, G. Mastin, and L. A. Romero, “Cellular-automata method for phase unwrapping,” J. Opt. Soc. Am. A4(1), 267–280 (1987). [CrossRef]
  22. A. Spik and D. W. Robinson, “Investigation of the cellular automata method for phase unwrapping and its implementation on an array processor,” Opt. Lasers Eng.14(1), 25–37 (1991). [CrossRef]
  23. H. Y. Chang, C. W. Chen, C. K. Lee, and C. P. Hu, “The Tapestry Cellular Automata phase unwrapping algorithm for interferogram analysis,” Opt. Lasers Eng.30(6), 487–502 (1998). [CrossRef]
  24. K. A. Stetson, J. Wahid, and P. Gauthier, “Noise-immune phase unwrapping by use of calculated wrap regions,” Appl. Opt.36(20), 4830–4838 (1997). [CrossRef] [PubMed]
  25. R. Goldstein, H. Zebker, and C. Werner, “Satellite radar interferometry: two-dimensional phase unwrapping,” Radio Sci.23(4), 713–720 (1988). [CrossRef]
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