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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 5 — Mar. 11, 2013
  • pp: 5346–5362
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Transport-of-intensity phase imaging using Savitzky-Golay differentiation filter - theory and applications

Chao Zuo, Qian Chen, Yingjie Yu, and Anand Asundi  »View Author Affiliations


Optics Express, Vol. 21, Issue 5, pp. 5346-5362 (2013)
http://dx.doi.org/10.1364/OE.21.005346


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Abstract

Several existing strategies for estimating the axial intensity derivative in the transport-of-intensity equation (TIE) from multiple intensity measurements have been unified by the Savitzky-Golay differentiation filter - an equivalent convolution solution for differentiation estimation by least-squares polynomial fitting. The different viewpoint from the digital filter in signal processing not only provides great insight into the behaviors, the shortcomings, and the performance of these existing intensity derivative estimation algorithms, but more important, it also suggests a new way of improving solution strategies by extending the applications of Savitzky-Golay differentiation filter in TIE. Two novel methods for phase retrieval based on TIE are presented - the first by introducing adaptive-degree strategy in spatial domain and the second by selecting optimal spatial frequencies in Fourier domain. Numerical simulations and experiments verify that the second method outperforms the existing methods significantly, showing reliable retrieved phase with both overall contrast and fine phase variations well preserved.

© 2013 OSA

1. Introduction

Phase measurement plays a prominent role in many fields of physics, such as optics [1

1. E. D. Barone-Nugent, A. Barty, and K. A. Nugent, “Quantitative phase-amplitude microscopy I: optical microscopy,” J. Microsc. 206(3), 194–203 (2002). [CrossRef] [PubMed]

], electron- and X-ray microscopy [2

2. S. Bajt, A. Barty, K. A. Nugent, M. McCartney, M. Wall, and D. Paganin, “Quantitative phase-sensitive imaging in a transmission electron microscope,” Ultramicroscopy 83(1-2), 67–73 (2000). [CrossRef] [PubMed]

, 3

3. T. E. Gureyev and S. W. Wilkins, “On X-ray phase retrieval from polychromatic images,” Opt. Commun. 147(4-6), 229–232 (1998). [CrossRef]

], diffraction [4

4. G. Popescu, T. Ikeda, R. R. Dasari, and M. S. Feld, “Diffraction phase microscopy for quantifying cell structure and dynamics,” Opt. Lett. 31(6), 775–777 (2006). [CrossRef] [PubMed]

]. Many samples such as optical elements, biological soft tissues, and cells are phase objects with little intensity variation for conventional brightfield microscopic imaging. Digital holography, as a paradigm of interferometric techniques is the primary quantitative technique for phase reconstruction with sub-wavelength accuracy [5

5. P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005). [CrossRef] [PubMed]

]. Recently, however, direct phase retrieval from intensity measurements using the Transport-of-intensity equation (TIE) [6

6. M. Reed Teague, “Deterministic phase retrieval: a Green's function solution,” J. Opt. Soc. Am. 73(11), 1434–1441 (1983). [CrossRef]

, 7

7. N. Streibl, “Phase imaging by the transport equation of intensity,” Opt. Commun. 49(1), 6–10 (1984). [CrossRef]

] derived from the free space Helmholtz wave equation in the paraxial wave approximation has gained increasing attention. The TIE-based method has the advantages of being non-interferometric [1

1. E. D. Barone-Nugent, A. Barty, and K. A. Nugent, “Quantitative phase-amplitude microscopy I: optical microscopy,” J. Microsc. 206(3), 194–203 (2002). [CrossRef] [PubMed]

], not needing phase unwrapping [8

8. A. Barty, K. A. Nugent, D. Paganin, and A. Roberts, “Quantitative optical phase microscopy,” Opt. Lett. 23(11), 817–819 (1998). [CrossRef] [PubMed]

], and applicable with partially coherent beams [9

9. D. Paganin and K. A. Nugent, “Noninterferometric phase imaging with partially coherent light,” Phys. Rev. Lett. 80(12), 2586–2589 (1998). [CrossRef]

, 10

10. A. M. Zysk, R. W. Schoonover, P. S. Carney, and M. A. Anastasio, “Transport of intensity and spectrum for partially coherent fields,” Opt. Lett. 35(13), 2239–2241 (2010). [CrossRef] [PubMed]

]. Thus phase reconstruction is now available over wide range of light- and electron-beam imaging systems without significant hardware modification and complicated computation [11

11. S. S. Gorthi and E. Schonbrun, “Phase imaging flow cytometry using a focus-stack collecting microscope,” Opt. Lett. 37(4), 707–709 (2012). [CrossRef] [PubMed]

, 12

12. L. Waller, S. S. Kou, C. J. R. Sheppard, and G. Barbastathis, “Phase from chromatic aberrations,” Opt. Express 18(22), 22817–22825 (2010). [CrossRef] [PubMed]

].

TIE has its basis in the relationship between phase and the first derivative of intensity along the optical axis. Therefore, an accurate estimate of intensity derivative should be obtained in order to solve the partial differential equation. However, the intensity derivative along the optic axis cannot be directly measured. Conventionally, the TIE approach involves recording two out-of-focus images, which usually being recorded symmetrically over- and under-focused by the same focal step from the in-focus image [13

13. M. Beleggia, M. A. Schofield, V. V. Volkov, and Y. Zhu, “On the transport of intensity technique for phase retrieval,” Ultramicroscopy 102(1), 37–49 (2004). [CrossRef] [PubMed]

15

15. K. Ishizuka and B. Allman, “Phase measurement of atomic resolution image using transport of intensity equation,” J. Electron Microsc. (Tokyo) 54(3), 191–197 (2005). [CrossRef] [PubMed]

]. These are then used to approximate the derivative of the image intensity in the direction of the beam propagation. However, the axial intensity derivative approximated by the finite difference method from two intensity measurements only achieves a maximum of second-order precision with respect to the separation of the adjacent intensity measurements under no noise condition [13

13. M. Beleggia, M. A. Schofield, V. V. Volkov, and Y. Zhu, “On the transport of intensity technique for phase retrieval,” Ultramicroscopy 102(1), 37–49 (2004). [CrossRef] [PubMed]

, 15

15. K. Ishizuka and B. Allman, “Phase measurement of atomic resolution image using transport of intensity equation,” J. Electron Microsc. (Tokyo) 54(3), 191–197 (2005). [CrossRef] [PubMed]

]. When noise exists which is natural, this method is rather vulnerable, necessitating large defocus distances in order to increase the signal-to-noise ratio (SNR) at the expense of phase resolution [14

14. D. Paganin, A. Barty, P. J. McMahon, and K. A. Nugent, “Quantitative phase-amplitude microscopy. III. The effects of noise,” J. Microsc. 214(1), 51–61 (2004). [CrossRef] [PubMed]

]. To overcome this difficulty, intensity measurements in multiple planes have been proposed to minimize the effect of the noise on the retrieved phase [16

16. M. Soto and E. Acosta, “Improved phase imaging from intensity measurements in multiple planes,” Appl. Opt. 46(33), 7978–7981 (2007). [CrossRef] [PubMed]

], reduce the impact of the higher order axial intensity derivatives [15

15. K. Ishizuka and B. Allman, “Phase measurement of atomic resolution image using transport of intensity equation,” J. Electron Microsc. (Tokyo) 54(3), 191–197 (2005). [CrossRef] [PubMed]

, 17

17. L. Waller, L. Tian, and G. Barbastathis, “Transport of Intensity phase-amplitude imaging with higher order intensity derivatives,” Opt. Express 18(12), 12552–12561 (2010). [CrossRef] [PubMed]

], or both [17

17. L. Waller, L. Tian, and G. Barbastathis, “Transport of Intensity phase-amplitude imaging with higher order intensity derivatives,” Opt. Express 18(12), 12552–12561 (2010). [CrossRef] [PubMed]

, 18

18. R. Bie, X.-H. Yuan, M. Zhao, and L. Zhang, “Method for estimating the axial intensity derivative in the TIE with higher order intensity derivatives and noise suppression,” Opt. Express 20(7), 8186–8191 (2012). [CrossRef] [PubMed]

]. In addition, these methods have been extended to the situation in which the intensity is measured in unequally-spaced planes [19

19. B. Xue, S. Zheng, L. Cui, X. Bai, and F. Zhou, “Transport of intensity phase imaging from multiple intensities measured in unequally-spaced planes,” Opt. Express 19(21), 20244–20250 (2011). [CrossRef] [PubMed]

, 20

20. S. Zheng, B. Xue, W. Xue, X. Bai, and F. Zhou, “Transport of intensity phase imaging from multiple noisy intensities measured in unequally-spaced planes,” Opt. Express 20(2), 972–985 (2012). [CrossRef] [PubMed]

]. While these strategies have been shown to work well in many situations, their performance, however, depends heavily on the noise level and the characteristics of the experimental data [18

18. R. Bie, X.-H. Yuan, M. Zhao, and L. Zhang, “Method for estimating the axial intensity derivative in the TIE with higher order intensity derivatives and noise suppression,” Opt. Express 20(7), 8186–8191 (2012). [CrossRef] [PubMed]

, 20

20. S. Zheng, B. Xue, W. Xue, X. Bai, and F. Zhou, “Transport of intensity phase imaging from multiple noisy intensities measured in unequally-spaced planes,” Opt. Express 20(2), 972–985 (2012). [CrossRef] [PubMed]

]. With a given set of intensity measurements, making an appropriate choice of a suitable algorithm is difficult. Besides, a comprehensive framework to better understand and, potentially, to improve existing derivative estimation algorithms is still lacking.

The aim of this paper is to show that great insight can be gained into the properties, the shortcomings, and the performance of these existing intensity derivative estimation algorithms by unifying them into the category of Savitzky-Golay filter. Specifically, it is shown that all the above mentioned methods can be viewed as special cases of the Savitzky-Golay differentiation filter. Based on these findings, two novel phase retrieval algorithms that extend the applications of the Savitzky-Golay differentiation filters in TIE are introduced. The adaptive-degree Savitzky-Golay differentiation filter method makes possible the choice of the distinct filter degree in different part of the intensity image. The second algorithm, which we call it optimal frequency selection automatically chooses the best parts of the phases obtained from Savitzky-Golay differentiation filters with various degrees by frequency decomposition. Extensive numerical studies and experiments are carried out to confirm and validate the proposed approaches.

2. Problem formulation

2.1 Transport of intensity equation

With the paraxial approximation, the derivative of intensity in the light propagation direction contains phase information that can be retrieved via TIE [6

6. M. Reed Teague, “Deterministic phase retrieval: a Green's function solution,” J. Opt. Soc. Am. 73(11), 1434–1441 (1983). [CrossRef]

]:
kI(r)z=[I(r)ϕ(r)],
(1)
Where k is the wave number 2π/λ, ris the position vector representing the spatial coordinates (x,y).is the gradient operator over r. zdenotes the optical axis, perpendicular to the x-y plane. The intensity derivative I(r)/z is commonly obtained by an intensity difference between two side planes, separated by Δz [13

13. M. Beleggia, M. A. Schofield, V. V. Volkov, and Y. Zhu, “On the transport of intensity technique for phase retrieval,” Ultramicroscopy 102(1), 37–49 (2004). [CrossRef] [PubMed]

, 14

14. D. Paganin, A. Barty, P. J. McMahon, and K. A. Nugent, “Quantitative phase-amplitude microscopy. III. The effects of noise,” J. Microsc. 214(1), 51–61 (2004). [CrossRef] [PubMed]

]
I(r)zI(r,Δz)I(r,Δz)2Δz,
(2)
This simple central difference formula gives the estimate of the derivative, where the error is the second order of the focus distance if the data are noise-free. Mathematically, the smaller the defocus distance is, the more accurate the Eq. (2) can be approximated to the ideal derivative. However, real measurements yield data with noise and discretization which does not allow Δz to be too small. Thus a compromise is made where Δz is chosen to balance the high-order (or non-linearity) error and the noise effect [14

14. D. Paganin, A. Barty, P. J. McMahon, and K. A. Nugent, “Quantitative phase-amplitude microscopy. III. The effects of noise,” J. Microsc. 214(1), 51–61 (2004). [CrossRef] [PubMed]

]. Specifically, the optimal Δz is dependent on both the maximum physically significant frequency of the object and the noise level [21

21. A. V. Martin, F. R. Chen, W. K. Hsieh, J. J. Kai, S. D. Findlay, and L. J. Allen, “Spatial incoherence in phase retrieval based on focus variation,” Ultramicroscopy 106(10), 914–924 (2006). [CrossRef] [PubMed]

]. A priori knowledge of these two aspects is difficult not be known in advance.

2.1 Multiple-plane schemes for derivative estimation

To improve the accuracy of derivative estimation, multiple-plane schemes are proposed by taking the non-linearity error or/and noise effect into account. The high-order finite difference method was first used by Ishizuka and Allman [15

15. K. Ishizuka and B. Allman, “Phase measurement of atomic resolution image using transport of intensity equation,” J. Electron Microsc. (Tokyo) 54(3), 191–197 (2005). [CrossRef] [PubMed]

] and more recently generalized by Waller et al. [17

17. L. Waller, L. Tian, and G. Barbastathis, “Transport of Intensity phase-amplitude imaging with higher order intensity derivatives,” Opt. Express 18(12), 12552–12561 (2010). [CrossRef] [PubMed]

]. For symmetric differencing, i.e., given a data set I(r,iΔz),i=n,...,0,...,n. The high-order finite difference method using a Taylor expansion has the form
I(r)zi=nnaiI(r,iΔz)Δz,
(3)
with coefficients [22

22. L. N. Trefethen, Finite difference and spectral methods for ordinary and partial differential equations, unpublished text, available at http://web.comlab.ox.ac.uk/oucl/work/nick.trefethen/pdetext.html, 1996.

]
ai=i(1)i+1(n!)2(n+i)!(ni)!.
(4)
It is also possible to vary the defocus distanceΔz [19

19. B. Xue, S. Zheng, L. Cui, X. Bai, and F. Zhou, “Transport of intensity phase imaging from multiple intensities measured in unequally-spaced planes,” Opt. Express 19(21), 20244–20250 (2011). [CrossRef] [PubMed]

]. To do so, one replacesΔz by δz=lΔz(lΝ)and i by i=liin Eq. (3). By changing l, the defocus distance δzof different plane can be varied. In the following, we only consider the condition that the measured planes are spaced equally and symmetrically from the center plane.

Without considering the effect of noise, the estimation error of Eq. (3) is of order O(Δz2n) by using 2n images. However, as with the two-plane method, one is faced with the unavoidable problem of noise and the quantitation error. To reduce the effect of noise, Soto and Acosta [16

16. M. Soto and E. Acosta, “Improved phase imaging from intensity measurements in multiple planes,” Appl. Opt. 46(33), 7978–7981 (2007). [CrossRef] [PubMed]

] proposed a noise-reduction finite difference formula which with coefficients

ai=3in(n+1)(2n+1).
(5)

The coefficients are derived by minimizing the noise effect (more specifically, the noise reduction ratio (NRR) [23

23. S. J. Orfanidis, Introduction to Signal Processing (Prentice-Hall, Inc., 1995).

] defined by i=nnai2) while ignoring the effect of all high-order terms in the Taylor expansion. The high-order finite difference indicates that the planes close to the center have to assume much larger weight than the ones further away, while the noise-reduction method makes the coefficients to be distributed as evenly as possible. Therefore, these two methods are contradictory in a sense. To balance this contradiction, Bie et al. [18

18. R. Bie, X.-H. Yuan, M. Zhao, and L. Zhang, “Method for estimating the axial intensity derivative in the TIE with higher order intensity derivatives and noise suppression,” Opt. Express 20(7), 8186–8191 (2012). [CrossRef] [PubMed]

] presented a higher order finite difference with noise-reduction which blends these two methods together, taking both the higher-order terms and noise effect into account. For the data from 2n+1 planes, the high-order terms are cancelled until mth order (m<2n+1), leaving some degree of freedom for noise suppression. In [18

18. R. Bie, X.-H. Yuan, M. Zhao, and L. Zhang, “Method for estimating the axial intensity derivative in the TIE with higher order intensity derivatives and noise suppression,” Opt. Express 20(7), 8186–8191 (2012). [CrossRef] [PubMed]

], the author did not give the explicit formula of these coefficients but showed the problem is well-posed when m<2n+1.

2.2 Main connections with Savitzky-Golay filters

After reviewing those multiple-plane schemes, one issue that arises is that it is not easy to determine which method should be used and is there any interrelation between these methods. Let us start with the Waller’s least-squares fitting method [17

17. L. Waller, L. Tian, and G. Barbastathis, “Transport of Intensity phase-amplitude imaging with higher order intensity derivatives,” Opt. Express 18(12), 12552–12561 (2010). [CrossRef] [PubMed]

]. Given 2n+1 data points {I[i]}={IiΔz|nin}, the least-squares polynomial of degree m (m<2n+1) has the form
Pm(i)=k=0mbkik.
(6)
In the normal least-squares fitting procedure, the coefficients bk are uniquely obtained by solving the normal equations of the Vandermonde system which minimize the sum of square of the residual (SSR), defined as SSRm=i=nn{Pm(i)I[i]}2. We then define a 2n+1 by m+1 matrix A={αi,j}as the matrix with elementsαi,j=ij,nin,j=0,1,...,m, the normal equations for the least-squares problem can be written in matrix form as
ATAb=ATI,
(7)
Where vector I={I[n],...,I[1],I[0],I[1],...,I[n]}Tand b={b0,b1,...,bm}T. The solution for the polynomial coefficients can thus be written as
b=(ATA)1ATI=HI.
(8)
The above formula gives all the polynomial coefficients b={b0,b1,...,bm}T. But the term of interest here is only the polynomial at z=0. Specifically, the first order derivative evaluated at z=0 requires an expression for coefficientb1 only. This allows Eq. (8) to be reduced to an expression of the form
b1=i=nnh1,iI[n],
(9)
where h1,i is the element of the second row of H. An important observation is that the matrix H is independent of the input samples. Thus, we can think of the first order derivative estimation using least-squares fitting as a shift-invariant discrete convolution process, which share the same format with finite difference. Indeed as early as 1964, Savitzky and Golay [24

24. A. Savitzky and M. J. E. Golay, “Smoothing and differentiation of data by simplified least squares Procedures,” Anal. Chem. 36(8), 1627–1639 (1964). [CrossRef]

] showed that fitting a polynomial to a set of input samples and then evaluating the resulting polynomial at a single point within the approximation interval is equivalent to discrete convolution with a fixed impulse response. The filters, obtained by this method are widely known (especially among chemists) as Savitzky-Golay filters [24

24. A. Savitzky and M. J. E. Golay, “Smoothing and differentiation of data by simplified least squares Procedures,” Anal. Chem. 36(8), 1627–1639 (1964). [CrossRef]

]. More generally, to evaluate the sth derivative at point t using a polynomial of degree m on 2n+1 data points, the convolution weight at point i can be calculated as [25

25. P. A. Gorry, “General least-squares smoothing and differentiation of nonuniformly spaced data by the convolution method,” Anal. Chem. 63(5), 534–536 (1991). [CrossRef]

]
hs,i=k=0m(2k+1)(2n)(k)(2n+k+1)(k+1)Pkn(i)Pkn,s(t),
(10)
where (a)(b)is a generalized factorial function (a)(a1)...(ab+1), and (a)(0)=0, and the Pkn(t)is the Gram polynomials defined as
Pkn(i)=j=0k(1)j+k(j+k)(2j)(m+i)(j)(j!)2(2m)(j),
(11)
and

Pkn,s(t)=(dsdxsPkn(t))x=t.
(12)

We are now ready to establish the correspondence between the different finite difference methods and the Savitzky-Golay differentiation filter (SGDF), i.e. least-squares fitting method. For the case that the center point is the evaluated point, the variable t is set to zero. Let s=1 for the 1st derivative, the weight becomes
h1,i=k=0m(2k+1)(2n)(k)(2n+k+1)(k+1)Pkn(i)Pkn,1(0).
(13)
Then we let the degreem=2n, through proper simplification. Equation (13) can be reduced to
h1,i=i(1)i+1(n!)2(n+i)!(ni)!.
(14)
Compare it with Eq. (4), we then have:

Observation 1: The high-order finite difference method corresponds to the SGDF with degree 2n.

Observation 2: The noise-reduction finite difference method corresponds to the SGDF with degree 1.

So far, we may instinctively perceive that there should be a connection between Bie’s method with Savitzky-Golay filter, since the two parents it inherited from are both particular cases of SGDF. But we cannot just follow the above routine because there is no explicit formula for Bie’s method. Here we give this observation first and detailed prove will be given in Appendix A.

Observation 3: The higher order finite difference with noise-reduction method corresponds to the SGDF with degree m (m<2n+1).

Finally, we have to mention that the unequally spaced multi-plane methods [19

19. B. Xue, S. Zheng, L. Cui, X. Bai, and F. Zhou, “Transport of intensity phase imaging from multiple intensities measured in unequally-spaced planes,” Opt. Express 19(21), 20244–20250 (2011). [CrossRef] [PubMed]

, 20

20. S. Zheng, B. Xue, W. Xue, X. Bai, and F. Zhou, “Transport of intensity phase imaging from multiple noisy intensities measured in unequally-spaced planes,” Opt. Express 20(2), 972–985 (2012). [CrossRef] [PubMed]

] fall in the class of SGDF without exception, since the Savitzky-Golay filter has also been generalized for unequally or non-uniformly spaced data as its offspring [23

23. S. J. Orfanidis, Introduction to Signal Processing (Prentice-Hall, Inc., 1995).

, 25

25. P. A. Gorry, “General least-squares smoothing and differentiation of nonuniformly spaced data by the convolution method,” Anal. Chem. 63(5), 534–536 (1991). [CrossRef]

]. Even the two plane method (Eq. (2)) can be viewed as a special case of SGDF.

2.3 Property of Savitzky-Golay differentiation filters

In last subsection, all of the above derivative estimation strategies used in TIE are unified by the SGDF - an equivalent convolution solution for differentiation of data by a least-squares polynomial fitting. Therefore, it is necessary to examine the property of SGDF first. The SGDF have many good properties [26

26. J. Luo, K. Ying, P. He, and J. Bai, “Properties of Savitzky–Golay digital differentiators,” Digit. Signal Process. 15(2), 122–136 (2005). [CrossRef]

]: (1) the convolution operation is quite straightforward and much easier to implement than the standard least-squares fitting; (2) the coefficients can be easily obtained using a look-up table generated from the explicit solution, or pre-calculated using existing routines; (3) most importantly, they are the optimal differentiation filters that minimize the NRR, subject to moments preservation constraints [23

23. S. J. Orfanidis, Introduction to Signal Processing (Prentice-Hall, Inc., 1995).

].

Another important issue is the accuracy of the derivative estimate using SGDF. For this prior information about the ideal signal is necessary to draw a reliable conclusion [16

16. M. Soto and E. Acosta, “Improved phase imaging from intensity measurements in multiple planes,” Appl. Opt. 46(33), 7978–7981 (2007). [CrossRef] [PubMed]

]. The Gauss–Markov theorem states that the mth degree SGDF is an unbiased estimator of the derivative, and can achieve the Cramer-Rao lower bound if the signal can be perfectly modeled by an m-order polynomial and the observed data are with independent Gaussian white noise [26

26. J. Luo, K. Ying, P. He, and J. Bai, “Properties of Savitzky–Golay digital differentiators,” Digit. Signal Process. 15(2), 122–136 (2005). [CrossRef]

]. However, in practice, the signal order is unknown and hence using a non-optimal degree will inevitably affect the accuracy and the effect of noise reduction. So the problem now we facing is to choose a polynomial with proper degree which fit a data set best. This problem appears new but is in fact similar to one mentioned in subsection 2.2. Selecting a suitable multi-plane method is similar to determining the degree of the SGDF.

3. Applications of SGDF in phase retrieval by TIE

3.1 Derivative estimation using adaptive-degree SGDF

3.2 Phase retrieval by optimal frequency selection

The adaptive-degree SGDF can give more accurate derivative estimates by finding a polynomial that fits the data of each pixel best. However, it does not guarantee a more accurate retrieved phase by applying TIE to this derivative estimate. In fact, the adaptive-degree SGDF usually gives an unsatisfactory phase result with severe patch effects - the result resembles a composite image that puts different pieces of estimated phases using different orders together (see Sections 4 and 5 for details), which is obviously not what we want. Because previously we concentrated our attention on the derivative estimation only, without considering the phase retrieval process using TIE. In this subsection, we present an alternative method which solves the optimal phase retrieval problem in spatial frequency domain.

Considering a thin object located at plane z = 0, illuminated with a monochromatic plane wave, the complex wave field in the focused plane can be written as (with no loss of generality, a single transverse dimension is used for notational simplicity)
U(x,0)=|U(x,0)|exp[iϕ(x)]=exp[iϕ(x)μ(x)],
(20)
whereμ(x)=0.5ln|U(x,0)|2. By applying the weak phase object approximation, the intensity image captured at zcan be represented by [14

14. D. Paganin, A. Barty, P. J. McMahon, and K. A. Nugent, “Quantitative phase-amplitude microscopy. III. The effects of noise,” J. Microsc. 214(1), 51–61 (2004). [CrossRef] [PubMed]

, 28

28. K. A. Nugent, T. E. Gureyev, D. J. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996). [CrossRef] [PubMed]

, 30

30. J. M. Cowley, Diffraction Physics, 2 ed. (North-Holland Pub. Co, 1993).

]
I(x,z)=|U(x,z)|2=12μ(x)Fcos(πλzu2)2ϕ(x)Fsin(πλzu2).
(21)
The intensity derivative estimated by a fixed-degree SGDF with 2n+1intensity measurements is
I(x)zi=nnaiI(x,iΔz)Δz=i=nnaii=nnai{2μ(x)Fcos(iΔzπλu2)+2ϕ(x)Fsin(iΔzπλu2)}Δz.
(22)
As we mentioned in Sec. 2.3, the coefficients of SGDF {ai|nin} are odd-symmetric with the central weight always null. Using these properties, the Fourier representation of Eq. (22) can be reduced to
FI(x)z2Fϕ(x)Δzi=nn[aisin(iΔzπλu2)]=2Fϕ(x)jΔzHSG(ejω)|ω=Δzπλu2,
(23)
where HSG(ejω) is frequency response of the SGDF. Note the frequency response is simply the Fourier transform of the system's impulse response, and here we use the odd-symmetry property again. If we assume that the SGDF forms an ideal 1st derivative, i.e. HSG(ejω)|ω=Δzπλu2=jΔzπλu2, this gives
FI(x)z=2πλu2Fϕ(x),
(24)
This equation coincides with the Fourier representation of TIE equation for the case of uniform unit intensity [17

17. L. Waller, L. Tian, and G. Barbastathis, “Transport of Intensity phase-amplitude imaging with higher order intensity derivatives,” Opt. Express 18(12), 12552–12561 (2010). [CrossRef] [PubMed]

, 27

27. T. E. Gureyev and K. A. Nugent, “Rapid quantitative phase imaging using the transport of intensity equation,” Opt. Commun. 133(1-6), 339–346 (1997). [CrossRef]

, 28

28. K. A. Nugent, T. E. Gureyev, D. J. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996). [CrossRef] [PubMed]

]. However, an ideal 1st derivative can never be achieved due to the discrete nature of SGDF. Therefore, the actual phase we obtain using SGDF is
ϕ^(x)=F1{Fϕ(x)HSG(ejω)|ω=Δzπλu2jΔzπλu2}=ϕ(x)F1{HLP(ejω)|ω=Δzπλu2},
(25)
which is a low-pass filtered version of the ideal phase ϕ(x). Interestingly, the frequency response of the low-pass filter HLP(ejω) is exactly the same as Eq. (16). Equation (25) suggests that the frequency characteristic of the retrieved phase ϕ^(x) using SGDF is determined by the frequency response of the low-pass filter that is implicit in the SGDF. Now, reconsidering Fig. 1(b), which indicates the effect of low-pass filter decreases with increasing degree of SGDF, one may think that a higher-degree SGDF gives a more intact phase (with wider range of spatial frequencies can be accurately recovered) than a lower degree one. However, the higher degree SGDF results in larger NRR than the one assumes a smaller order. In another word, smaller order filters always give estimates of the low-frequency phase with higher SNR, but they suffer from the problem of not having enough response for high frequency phase variations. The above discussion reaffirmed that using a single degree of SGDF cannot offer the optimal solution and a tradeoff between phase information at high and low spatial frequencies is necessary.

Rather than choosing the optimal filter degree on a pixel-by-pixel basis, here we solve the optimal phase retrieval problem in the spatial frequency domain. The basic idea is to extract the best frequency components of the phase images obtained from SGDFs with various degrees and then recombine them into a composite phase. Considering the phase information lies within a certain bandwidth of spatial frequencies, such that it can be correctly reconstructed by both a smaller degree SGDF and a higher degree one. The smaller degree filter is preferred for its low NRR, or in another word, higher SNR. The frequency components of higher degree SGDF is only used when the smaller degree filter fails to retrieve the phase information reliably. Thus, a rigorous definition of ‘correct reconstruction’ or ‘reliable retrieval’ is necessary.

Most frequently, a 3dB-point is chosen as a boundary in a filter's frequency response at which the signal through the system begins to be attenuated rather than passing through. In our case, we prefer a stricter boundary point at 0.3dB - amplitude of the filter falls to the 96.6% of the pass band. This means all spatial frequencies of the phase below 0.3dB cutoff frequency of the SGDF with one specific degree can be considered as reliable retrieval. The 0.3dB-points for filters with different n for odd polynomial orders m are displayed in Fig. 2
Fig. 2 Normalized 0.3 dB cutoff frequencies fc=ωc/π for different data half-length n and polynomial degree m.
(remember the SGDF yields equivalent results for the order 1/2, 3/4, 5/6,…). The points marked with * and connected by a blue line are the measured cutoff frequencies for a fixed value of half data length n. The values for short length filters are given more precisely in Table 1

Table 1. Normalized 0.3dB Cutoff Frequencies as a Function of m and n (n = 1~10)

table-icon
View This Table
. For large n, it is impractical to list all the 0.3dB values due to the limited space here. Fortunately, we found in all cases in Fig. 2, fc varies almost linearly with m when m<<2n with the slope being dependent inversely on n, but the curves for n<25 tend to deviate from a straight line as m increases. However, when m is large, e.g. 50 and 100, the linear region of the curve coincides with the range of usable values of m, so a nearly linear relation holds over a wide range of m. A reasonably accurate approximation to this behavior for the indicated range of parameters is
fc=m0.33.5n1,n25.
(26)
The values of fc predicted by this equation are marked with a circle and connected by a red dash line. It shows that this simple formula fits the true values quite well even for the case n=25 where the measurements deviate only slightly from the straight line over the entire range of m. The formula does not fit as well for values of m less than 25. However, the dependence of fc on m is still linear except for small n. For 10<n<25 and m suitably restricted, a formula similar to Eq. (26) with 1 in the denominator replaced by 2.5 gives more accurate predictions. While a more complicated functional form based on more measurements could provide more accurate predictions over a wider range of m and n, Eq. (26) should be adequate for most applications.

To decompose and reconstruct the retrieved phase in frequency domain according to the cutoff frequency obtained, a complementary filter bank can be designed with the frequency response Hm(ejω),m=1,3,...2n1 and their amplitudes sum to unity. To achieve a flat response in the pass band and a fast roll off, the Hm(ejω) can be in the form of high-order Butterworth filters or even ideal ones. For m=3,5,...,2n3 the filters Hk(ejω) are band-pass and have the pass band from fcm to fcm1, where fcm represents the 0.3dB cutoff of mth degree SGDF. For m=1 the filter is a low-pass with the cutoff frequency fc1 and for m=2n1 the filter is a high-pass with the cutoff frequency fc2n1. The cutoff frequency of SGDF can be obtained by referring to Table 1, or using Eq. (26) for larger n. The phase retrieved by the mth order SGDFϕ^m(x) is then filtered by Hm(ejω)
ϕm(x)=ϕ^m(x)F1[Hm(ejω)|ω=Δzπλu2]=F1{F[I(x)z]mHm(ejω)|ω=Δzπλu2Δzπλu2},
(27)
and the final reconstructed phase ϕ(x) is their summation
ϕ(x)=ϕ1(x)+ϕ3(x)+...+ϕ2n1(x),
(28)
where the [I(x)/z]mis the intensity derivative estimate using mth degree SGDF. The filter can either be applied to the phase obtained by TIE or together with the inverse Laplacian when solving the TIE in Fourier domain. A flowchart schematic of the whole procedure, which we call as optimal frequency selection (OFS) is shown in Fig. 3
Fig. 3 A flowchart schematic of the OFS algorithm.
. Note usually the final reconstructed phase is not necessarily a composite of all possible orders because of the sampling effect and the limited physically significant frequency of the object. Therefore, we recommend the summation of Eq. (28) begins from ϕ1(x), ϕ3(x),… and stops when the mean absolute of ϕm(x)is below a given value, or the cutoff frequency with respect to u (i.e. fcm/Δzλ) exceeds the maximal range of the frequency coordinate in the Fourier domain corresponding to x. Finally, to reduce the computation load when n is large; the filter order can be increased with a step of four rather than two without sacrificing the accuracy too much.

4. Simulations

To test the performance of the algorithm, we simulated the propagation of a complex field of a pure phase profile of the letters ‘TIE’ (shown in Fig. 4(a)
Fig. 4 Phase recovery comparison of the synthetic noisy test data; (a) true phase, (b) traditional TIE using two planes separated by 20μm (RMSE = 0.2552); (c) 1st fixed degree SGDF (RMSE = 0.0128); (d) 31st fixed degree SGDF (RMSE = 0.0651); (e) 7th fixed degree SGDF (RMSE = 0.0092); (f) traditional TIE using two planes separated by 100μm (RMSE = 0.0115); (g) adaptive-degree SGDF (RMSE = 0.0453); (h) OFS (RMSE = 0.0020); the square areas with red dot lines are magnified for clarity.
) which was defined on a grid with 256×256 pixels with a pixel size of 2μm×2μm. The wavelength is 632.8nm. Fifty one intensity images separated by 10μm were generated with defocused range between and −250μm ~250μm. To simulate noise effect, each defocused image was corrupted by Gaussian noise with standard deviation of 0.002. The Poisson noise case has been also tested on the same data. The results did not differ significantly from the case of Gaussian and hence not discussed further in this work.

To better explain the low error achieved by the OFS method, we show the RMSE curve versus the filter degree in Fig. 6
Fig. 6 (Media 1) The curve of RMSE evolution, shown with corresponding magnified areas.
. A video sequence (Media 1) is also presented to show the evolution of the phase reconstruction process. It can be seen the phase resolution gradually improved by adding high-frequency information corresponding to the optimal frequency bands in the higher order results. The reconstruction process converged when degree m = 31, so the final result was a composite of phases from the 1st to 31st orders. For simplicity, an ideal band-pass filter bank was used to decompose and extract the optimal spatial frequency component of each phase recovered. The Gibbs phenomenon combined with the diffraction ringing effect was clearly visible during the first few steps in reconstruction as ripples extending beyond the object region, but it faded out rapidly as the filter degree increases. The final reconstructed result shows a rather clean phase with fairly high resolution, which proves that our method automatically sieve the best parts of result from different orders without any over-fitting or under-fitting problems.

The OFS method always performs better than other methods using a fixed order because it chooses the frequency-bands optimally with highest SNR in the results of different degrees. Besides, the curve of OFS is also rather flat despite of a slight increase with the noise variance, showing its great adaptability in noisy situations. From these results, we can safely conclude that the OFS method removes the guesswork in the choice of polynomial degree and significantly outperforms the fixed-order method even with the best selection of polynomial degree.

5. Experiments

The experimental test setup is shown in Fig. 8(a)
Fig. 8 (a) The experimental test setup, and (b) (Media 2) intensity images from the focus stack.
. A He-Ne laser (λ = 632.8nm) source is expanded and collimated and then illuminates the object under test. The object is reimaged onto a CCD via a 4f system - two lenses of focal length f = 25 mm separated by the distance 2f, and the distance from the object to the first lens is f. The camera is set on a translation stage in order to modify the defocus distance. The phase object under test is a geometry pattern etched on PMMA substrate, which is also shown in Fig. 8(a). Fifty-one images were captured with an equal separation of 50μm (Media 2). Some data samples from the intensity stack are shown in Fig. 8(b). All these images are recorded by a monochrome CCD imaging device (The Imaging Source DMK 41AU02, 4.65μm pixel size) and digitally processed using MATLAB.

The phases recovered by different methods are shown in Fig. 9
Fig. 9 Phase recovery comparison of a test phase object; (a) traditional TIE using two images separated by 100μm; (b) 1st fixed degree SGDF; (c) 5th fixed degree SGDF; (d) 15th fixed degree SGDF; (e) 27th fixed degree SGDF; (f) traditional TIE using two images separated by 700μm; (g) adaptive-degree SGDF; (h) OFS (Media 3); the bottom right red squares show the corresponding enlarged regions.
, which are similar with our simulation results. Smaller-order filters effectively suppress noise, at the expense of distorting smaller scale phase features. Higher-order filters preserve smaller scale features well, with the disadvantage of being less effective at suppressing low-frequency noise. By subjective evaluation of the reconstructed phase, it is noticed that the 5th degree SGDF produces best visual effect with regard to the contrast and resolution (Fig. 9(c)). Similarly, the optimum distance for traditional TIE (two-plane method) was found to be 700μm (Fig. 9(d)). However, either the background was too noisy or the phase resolution was sacrificed in their estimated phase (see Figs. 9(b)-(f)). The adaptive-degree SGDF method composited the phase in spatial domain, resulting in an unnatural result with contrast reversal (Figs. 9(g)). Figure 9(h) shows the reconstructed result by OFS, with the evolution process animated in Media 3. The final phase of OFS combined the results of different degrees up to 27, indicating that not only the tiny details were detected but also the overall contrast of the phase was well preserved, despite slight non-uniform distribution in the background (which may be partially caused by the dust particles on the sample and/or the lens).

To better quantify the phase retrieved by the proposed method, we also measured the same sample using a digital holography (DH) microscope based on the Michelson interferometric configuration with a 4x objective (NA = 0.13) [31

31. Q. Weijuan, C. O. Choo, Y. Yingjie, and A. Asundi, “Microlens characterization by digital holographic microscopy with physical spherical phase compensation,” Appl. Opt. 49(33), 6448–6454 (2010). [CrossRef] [PubMed]

]. Figure 10
Fig. 10 Comparison between the phases retrieved from digital holography and TIE; (a) hologram of the same test sample; (b) hologram Spectrum; (c) reconstructed phase from digital holography (the red dot square at bottom right shows a corresponding area of Fig. 9(h)); (d) cross-sections of the phase marked with the red and blue lines in (c).
shows the hologram (a) and its spectrum (b). The phase demodulation was done by first filtering one of the first orders (the red circle area) from the hologram spectrum and then reconstructing the hologram in the traditional manner [31

31. Q. Weijuan, C. O. Choo, Y. Yingjie, and A. Asundi, “Microlens characterization by digital holographic microscopy with physical spherical phase compensation,” Appl. Opt. 49(33), 6448–6454 (2010). [CrossRef] [PubMed]

]. The phase curvature was physically compensated by introducing the same spherical phase curvature in the reference beam [32

32. W. Qu, C. O. Choo, V. R. Singh, Y. Yingjie, and A. Asundi, “Quasi-physical phase compensation in digital holographic microscopy,” J. Opt. Soc. Am. A 26(9), 2005–2011 (2009). [CrossRef] [PubMed]

]. Figure 10(c) shows the recovered phase. It can be seen that the reconstructed phase surface using the OFS method agrees well with the phase measurements made by DH. To clearly compare the phase profiles, a magnification of the corresponding area from Fig. 9(h) is also shown in Fig. 10(c), in the red dot line rectangle. Figure 10(d) shows a comparison of phase profiles taken across the sample edge showing the phase jump obtained with holography and with our TIE approach identified by blue continuous and red dashed lines, respectively. Both two methods were capable of following the large phase step and fine phase variations (e.g. the vertical line nicks results from the imperfection in fabrication) of the sample quite accurately.

6. Conclusions and discussions

The contribution of this paper is two-fold. First, several existing strategies for estimating the axial intensity derivative in TIE from multiple intensity measurements have been unified by the SGDF, which not only provides different views and insights on known derivative estimation methods but, more important, it also suggests a new way of improving solution strategies from an angle of digital filter design in signal processing. Second, two advanced applications of SGDF in TIE have been proposed. The adaptive-degree SGDF method based on the statistical testing allows the automatic choice of the proper degree of SGDF in different parts of the intensity image. The OFS method, more desirably, decomposes, processes, and then recombines the phases using the SGDF with various degrees in spatial frequency domain. The introduction of OFS provides a powerful tool which, in all of our numerical simulations and experiments, outperforms both optimally chosen and sub-optimally chosen fixed-degree methods that are commonly used.

Although the OFS improves the noise resistibility and the phase resolution of TIE significantly, its performance also shares a common limitation of TIE based method - lower spatial frequencies are more sensitive to noise as compared with the higher spatial frequencies. Under severe noisy conditions, the low frequency noise is difficult to be entirely eliminated, because the inverse Laplacian operation is ill-conditioned at near zero-frequency. Figure 11
Fig. 11 Phase recovery results under significant noise (noise standard deviation 0.01); (a) intensity image with defocus distance 80μm, (b) 7th fixed degree SGDF (RMSE = 0.6016); (c) OFS (RMSE = 0.0616); (d) OFS with Tikhonov-regularization (RMSE = 0.0332).
show the simulation results when the noise was increased to significant level (standard deviation 0.01). As shown in Fig. 11(a), the underlying intensity signal was submerged in the noise, so that even the 7th fixed degree SGDF can no longer render a visible result (Fig. 11(b)). The OFS reduced the noise significantly, but the residual low-frequency noise remained obvious (Fig. 11(c)). In this situation, proper regularization treatments [33

33. S. S. Kou, L. Waller, G. Barbastathis, and C. J. R. Sheppard, “Transport-of-intensity approach to differential interference contrast (TI-DIC) microscopy for quantitative phase imaging,” Opt. Lett. 35(3), 447–449 (2010). [CrossRef] [PubMed]

, 34

34. L. Tian, J. C. Petruccelli, and G. Barbastathis, “Nonlinear diffusion regularization for transport of intensity phase imaging,” Opt. Lett. 37(19), 4131–4133 (2012). [CrossRef] [PubMed]

] can help to reduce the low frequency artifacts at the cost of attenuation in slow variations of the underlying true phase signal. By combining the Tikhonov-regularization with OFS, the low frequency noise further reduced, accompanied by some dark ‘halos’ appeared near edges (Fig. 11(d)). Another possible countermeasure is to increase the defocusing range of the intensity stack so that the cut-off frequency of 1st degree SGDF can be narrowed to near zero-frequency. Of course, this point is beyond the scope of the present work and needs more detailed investigation.

Appendix A: Proof of Observation 3

The optimization problem in higher order finite difference with noise-reduction method is to minimize NNR i=nnai2, under constraints that (1) i=nnai=0; (2) i=nnaii=1; (3) i=nnaiik=0,k=2,3,...,m. In [18

18. R. Bie, X.-H. Yuan, M. Zhao, and L. Zhang, “Method for estimating the axial intensity derivative in the TIE with higher order intensity derivatives and noise suppression,” Opt. Express 20(7), 8186–8191 (2012). [CrossRef] [PubMed]

], the authors solve this problem by matrix partition. Here we rewrite the problem as a quadratic programming problem:
{min12aTas.t.Ba=c,
(29)
where Ba=c is written as
[(n)0(1)0110n0(n)1(1)1011n1(n)2(1)2012n2(n)m(1)m01mnm][ana1a0a1an]=[0100].
(30)
Solving Eq. (29) using Legendre multiplier:
[EBTB0][aλL]=[0c],
(31)
where λLis the Legendre vector and Eis identity matrix. The solution for the equations is:
λL=(BBT)1canda=((BBT)1B)Tc.
(32)
Carefully examine the matrix B, we can find it equals ATin Eq. (7). Therefore,
a=((ATA)1AT)Tc=HTc,
(33)
where H is exactly the same one as in Eq. (8), and HTc extracts the second row from H. Thus, the Observation 3 is established.

Acknowledgments

This project was supported by the Research Fund for the Doctoral Program of Ministry of Education of China (No. 20123219110016) and the Research and Innovation Plan for Graduate Students of Jiangsu Higher Education Institutions, China (No. CXZZ11_0237). The author Chao Zuo gratefully acknowledges the financial support from China Scholarship Council (No. 201206840009).

References and links

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E. D. Barone-Nugent, A. Barty, and K. A. Nugent, “Quantitative phase-amplitude microscopy I: optical microscopy,” J. Microsc. 206(3), 194–203 (2002). [CrossRef] [PubMed]

2.

S. Bajt, A. Barty, K. A. Nugent, M. McCartney, M. Wall, and D. Paganin, “Quantitative phase-sensitive imaging in a transmission electron microscope,” Ultramicroscopy 83(1-2), 67–73 (2000). [CrossRef] [PubMed]

3.

T. E. Gureyev and S. W. Wilkins, “On X-ray phase retrieval from polychromatic images,” Opt. Commun. 147(4-6), 229–232 (1998). [CrossRef]

4.

G. Popescu, T. Ikeda, R. R. Dasari, and M. S. Feld, “Diffraction phase microscopy for quantifying cell structure and dynamics,” Opt. Lett. 31(6), 775–777 (2006). [CrossRef] [PubMed]

5.

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005). [CrossRef] [PubMed]

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M. Reed Teague, “Deterministic phase retrieval: a Green's function solution,” J. Opt. Soc. Am. 73(11), 1434–1441 (1983). [CrossRef]

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N. Streibl, “Phase imaging by the transport equation of intensity,” Opt. Commun. 49(1), 6–10 (1984). [CrossRef]

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A. Barty, K. A. Nugent, D. Paganin, and A. Roberts, “Quantitative optical phase microscopy,” Opt. Lett. 23(11), 817–819 (1998). [CrossRef] [PubMed]

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D. Paganin and K. A. Nugent, “Noninterferometric phase imaging with partially coherent light,” Phys. Rev. Lett. 80(12), 2586–2589 (1998). [CrossRef]

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A. M. Zysk, R. W. Schoonover, P. S. Carney, and M. A. Anastasio, “Transport of intensity and spectrum for partially coherent fields,” Opt. Lett. 35(13), 2239–2241 (2010). [CrossRef] [PubMed]

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S. S. Gorthi and E. Schonbrun, “Phase imaging flow cytometry using a focus-stack collecting microscope,” Opt. Lett. 37(4), 707–709 (2012). [CrossRef] [PubMed]

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L. Waller, S. S. Kou, C. J. R. Sheppard, and G. Barbastathis, “Phase from chromatic aberrations,” Opt. Express 18(22), 22817–22825 (2010). [CrossRef] [PubMed]

13.

M. Beleggia, M. A. Schofield, V. V. Volkov, and Y. Zhu, “On the transport of intensity technique for phase retrieval,” Ultramicroscopy 102(1), 37–49 (2004). [CrossRef] [PubMed]

14.

D. Paganin, A. Barty, P. J. McMahon, and K. A. Nugent, “Quantitative phase-amplitude microscopy. III. The effects of noise,” J. Microsc. 214(1), 51–61 (2004). [CrossRef] [PubMed]

15.

K. Ishizuka and B. Allman, “Phase measurement of atomic resolution image using transport of intensity equation,” J. Electron Microsc. (Tokyo) 54(3), 191–197 (2005). [CrossRef] [PubMed]

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M. Soto and E. Acosta, “Improved phase imaging from intensity measurements in multiple planes,” Appl. Opt. 46(33), 7978–7981 (2007). [CrossRef] [PubMed]

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L. Waller, L. Tian, and G. Barbastathis, “Transport of Intensity phase-amplitude imaging with higher order intensity derivatives,” Opt. Express 18(12), 12552–12561 (2010). [CrossRef] [PubMed]

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R. Bie, X.-H. Yuan, M. Zhao, and L. Zhang, “Method for estimating the axial intensity derivative in the TIE with higher order intensity derivatives and noise suppression,” Opt. Express 20(7), 8186–8191 (2012). [CrossRef] [PubMed]

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B. Xue, S. Zheng, L. Cui, X. Bai, and F. Zhou, “Transport of intensity phase imaging from multiple intensities measured in unequally-spaced planes,” Opt. Express 19(21), 20244–20250 (2011). [CrossRef] [PubMed]

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T. E. Gureyev and K. A. Nugent, “Rapid quantitative phase imaging using the transport of intensity equation,” Opt. Commun. 133(1-6), 339–346 (1997). [CrossRef]

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31.

Q. Weijuan, C. O. Choo, Y. Yingjie, and A. Asundi, “Microlens characterization by digital holographic microscopy with physical spherical phase compensation,” Appl. Opt. 49(33), 6448–6454 (2010). [CrossRef] [PubMed]

32.

W. Qu, C. O. Choo, V. R. Singh, Y. Yingjie, and A. Asundi, “Quasi-physical phase compensation in digital holographic microscopy,” J. Opt. Soc. Am. A 26(9), 2005–2011 (2009). [CrossRef] [PubMed]

33.

S. S. Kou, L. Waller, G. Barbastathis, and C. J. R. Sheppard, “Transport-of-intensity approach to differential interference contrast (TI-DIC) microscopy for quantitative phase imaging,” Opt. Lett. 35(3), 447–449 (2010). [CrossRef] [PubMed]

34.

L. Tian, J. C. Petruccelli, and G. Barbastathis, “Nonlinear diffusion regularization for transport of intensity phase imaging,” Opt. Lett. 37(19), 4131–4133 (2012). [CrossRef] [PubMed]

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.5070) Image processing : Phase retrieval
(120.5050) Instrumentation, measurement, and metrology : Phase measurement

ToC Category:
Image Processing

History
Original Manuscript: January 9, 2013
Revised Manuscript: February 8, 2013
Manuscript Accepted: February 13, 2013
Published: February 25, 2013

Citation
Chao Zuo, Qian Chen, Yingjie Yu, and Anand Asundi, "Transport-of-intensity phase imaging using Savitzky-Golay differentiation filter - theory and applications," Opt. Express 21, 5346-5362 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-5-5346


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References

  1. E. D. Barone-Nugent, A. Barty, and K. A. Nugent, “Quantitative phase-amplitude microscopy I: optical microscopy,” J. Microsc.206(3), 194–203 (2002). [CrossRef] [PubMed]
  2. S. Bajt, A. Barty, K. A. Nugent, M. McCartney, M. Wall, and D. Paganin, “Quantitative phase-sensitive imaging in a transmission electron microscope,” Ultramicroscopy83(1-2), 67–73 (2000). [CrossRef] [PubMed]
  3. T. E. Gureyev and S. W. Wilkins, “On X-ray phase retrieval from polychromatic images,” Opt. Commun.147(4-6), 229–232 (1998). [CrossRef]
  4. G. Popescu, T. Ikeda, R. R. Dasari, and M. S. Feld, “Diffraction phase microscopy for quantifying cell structure and dynamics,” Opt. Lett.31(6), 775–777 (2006). [CrossRef] [PubMed]
  5. P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett.30(5), 468–470 (2005). [CrossRef] [PubMed]
  6. M. Reed Teague, “Deterministic phase retrieval: a Green's function solution,” J. Opt. Soc. Am.73(11), 1434–1441 (1983). [CrossRef]
  7. N. Streibl, “Phase imaging by the transport equation of intensity,” Opt. Commun.49(1), 6–10 (1984). [CrossRef]
  8. A. Barty, K. A. Nugent, D. Paganin, and A. Roberts, “Quantitative optical phase microscopy,” Opt. Lett.23(11), 817–819 (1998). [CrossRef] [PubMed]
  9. D. Paganin and K. A. Nugent, “Noninterferometric phase imaging with partially coherent light,” Phys. Rev. Lett.80(12), 2586–2589 (1998). [CrossRef]
  10. A. M. Zysk, R. W. Schoonover, P. S. Carney, and M. A. Anastasio, “Transport of intensity and spectrum for partially coherent fields,” Opt. Lett.35(13), 2239–2241 (2010). [CrossRef] [PubMed]
  11. S. S. Gorthi and E. Schonbrun, “Phase imaging flow cytometry using a focus-stack collecting microscope,” Opt. Lett.37(4), 707–709 (2012). [CrossRef] [PubMed]
  12. L. Waller, S. S. Kou, C. J. R. Sheppard, and G. Barbastathis, “Phase from chromatic aberrations,” Opt. Express18(22), 22817–22825 (2010). [CrossRef] [PubMed]
  13. M. Beleggia, M. A. Schofield, V. V. Volkov, and Y. Zhu, “On the transport of intensity technique for phase retrieval,” Ultramicroscopy102(1), 37–49 (2004). [CrossRef] [PubMed]
  14. D. Paganin, A. Barty, P. J. McMahon, and K. A. Nugent, “Quantitative phase-amplitude microscopy. III. The effects of noise,” J. Microsc.214(1), 51–61 (2004). [CrossRef] [PubMed]
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