OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 33 — Nov. 20, 2010
  • pp: 6376–6384

Bispectral analysis and recovery of images distorted by a moving water surface

Zhiying Wen, Andrew Lambert, Donald Fraser, and Hongdong Li  »View Author Affiliations


Applied Optics, Vol. 49, Issue 33, pp. 6376-6384 (2010)
http://dx.doi.org/10.1364/AO.49.006376


View Full Text Article

Enhanced HTML    Acrobat PDF (735 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We propose a new algorithm to recover a geometrically correct image of an object or scene from a set of images distorted by the wave motion of a water surface. Under mild conditions where the wavy surface normals weakly satisfy a Gaussian distribution, we demonstrate that the geometric distortion can be removed and a corrected image can be recovered. Our method is based on higher-order spectra analysis—in particular, the bispectrum, similar to its use in astronomical speckle imaging. In adapting this technique to imaging through or over a moving water surface, special care must be taken, and specifically tailored techniques are discussed in this paper. Our algorithm has been tested under two different scenarios: the refraction of light through a water surface (the underwater case) and the reflection of light from a water surface (the reflection case). Results in both cases have been encouraging.

© 2010 Optical Society of America

OCIS Codes
(010.0010) Atmospheric and oceanic optics : Atmospheric and oceanic optics
(010.1330) Atmospheric and oceanic optics : Atmospheric turbulence
(100.0100) Image processing : Image processing
(100.3020) Image processing : Image reconstruction-restoration

ToC Category:
Image Processing

History
Original Manuscript: June 18, 2010
Manuscript Accepted: August 16, 2010
Published: November 10, 2010

Citation
Zhiying Wen, Andrew Lambert, Donald Fraser, and Hongdong Li, "Bispectral analysis and recovery of images distorted by a moving water surface," Appl. Opt. 49, 6376-6384 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-33-6376


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. M. Roggemann and B. Welch, Imaging Through Turbulence (CRC Press, 1996).
  2. M. L. Holohan and J. C. Dainty, “Low-order adaptive optics: a possible use in underwater imaging?” Opt. Laser Technol. 29, 51–55 (1997). [CrossRef]
  3. R. Shefer, M. Malhi, and A. Shenhar, “Waves distortion correction using cross correlation,” http://visl.technion.ac.il/projects/2000maor/ (2001).
  4. H. Murase, “Surface shape reconstruction of a nonrigid transparent object using refraction and motion,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 1045–1052 (1992). [CrossRef]
  5. A. Efros, V. Isler, J. Shi, and M. Visontai, “Seeing through water,” in Neural Information Processing Systems (NIPS, 2004).
  6. A. Donate, G. Dahme, and E. Ribeiro, “Classification of textures distorted by water waves,” in Proceedings of the 18th International Conference on Pattern Recognition (IEEE, 2006), pp. 421–424.
  7. A. Donate and E. Ribeiro, “Improved reconstruction of images distorted by water waves,” in Proceedings of the First International Conference on Computer Vision Theory and Applications (INSTICC, 2006), pp. 228–235
  8. D. M. Milder, P. W. Carter, N. L. Flacco, B. E. Hubbard, N. M. Jones, K. R. Panici, B. D. Platt, R. E. Potter, K. W. Tong, and D. J. Twisselmann, “Reconstruction of through-surface underwater imagery,” Waves Random Complex Media 16, 521–530(2006). [CrossRef]
  9. Y. Tian and S. Narasimhan, “Seeing through water: image restoration using model-based tracking,” Proceedings of the IEEE International Conference of Computer Vision (ICCV) (IEEE, 2009), pp. 2303–2310.
  10. Z. Wen, D. Fraser, and A. Lambert, “Bicoherence: a new lucky region technique in anisoplanatic image restoration,” Appl. Opt. 48, 6111–6119 (2009). [CrossRef] [PubMed]
  11. C. Cox and W. Munk, “Slopes of the sea surface deduced from photographs of sun glitter,” Scripps Inst. Oceanogr. 5, 401–479(1956).
  12. D. Fried, “Probability of getting a lucky short-exposure image through turbulence,” J. Opt. Soc. Am. 68, 1651–1658(1978). [CrossRef]
  13. R. Tubbs, “Lucky exposures: diffraction limited astronomical imaging through the atmosphere,” Ph.D. dissertation (Cambridge University, 2003).
  14. S. Weddell and R. Webb, “Data preprocessing on sequential data for improved astronomical imaging,” in Proceedings of Image and Vision Computing (Academic, 2005), pp. 1–8.
  15. N. Law, C. Mackay, and J. Baldwin, “Lucky imaging: high angular resolution imaging in the visible from the ground,” Astron. Astrophys. 446, 739–745 (2006). [CrossRef]
  16. Z. Wen, D. Fraser, and A. Lambert, “Bicoherence used to predict lucky regions in turbulence affected surveillance,” in Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (IEEE, 2006), p. 108. [CrossRef]
  17. C. J. Carrano, “Anisoplanatic performance of horizontal-path speckle imaging,” Proc. SPIE 5162, 14–26 (2003). [CrossRef]
  18. Z. Wang and A. Bovik, “A universal image quality index,” IEEE Signal Process Lett. 9, 81–84 (2002). [CrossRef]
  19. A. Lohmann and B. Wirnitzer, “Triple correlations,” in Proc. IEEE 72, 889–901 (1984). [CrossRef]
  20. C. Nikias and A. Petropulu, Higher-Order Spectra Analysis (PTR Prentice Hall, 1993).
  21. J. Fackrell and S. McLaughlin, “Quadratic phase coupling detection using higher order statistics,” in Proceedings of the IEE Colloquium on Higher Order Statistics (Academic, 1995), pp. 9–17. [CrossRef]
  22. J. Fackrell, S. McLaughlin, and P. White, “Practical issues concerning the use of the bicoherence for the detection of quadratic phase coupling,” in Proceedings of the IEEE Workshop on HOS (IEEE, 1995), pp. 1–5.
  23. M. Hinich and M. Wolinsky, “Normalizing bispectra,” J. Stat. Plan. Infer. 130, 405–411 (2005). [CrossRef]
  24. S. McLaughlin, A. Stogioglou, and J. Fackrell, “Introducing higher order statistics (HOS) for the detection of nonlinearities,” UK Nonlinear News (15 September 1995).
  25. W. Silva, T. Strganac, and M. Hajj, “Higher-order spectral analysis of a nonlinear pitch and plunge apparatus,” in Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (Academic, 2005), pp. 1–20. [PubMed]
  26. T. D. de Wit, “Spectral and statistical analysis of plasma turbulence: beyond linear techniques,” in Space Plasma Simulation, J.Büchner, C.T.Dum, and M.Scholer, eds. (Springer, 2003).
  27. H. Farid and A. Popescu, “Blind removal of image nonlinearities,” in Proceedings of the IEEE Conference on International Conference of Computer Vision (IEEE, 2001), pp. 76–81.
  28. Z. Wen, D. Fraser, A. Lambert, and H. Li, “Reconstruction of underwater image by bispectrum,” in Proceedings of the IEEE International Conference on Image Processing 2007 (IEEE, 2007), Vol. 3, pp. 545–548.
  29. D. Fraser, G. Thorpe, and A. Lambert, “Atmospheric turbulence visualization with wide-area motion-blur restoration,” J. Opt. Soc. Am. A 16, 1751–1758 (1999). [CrossRef]
  30. C. Matson, “Weighted-least-squares phase reconstruction from the bispectrum,” J. Opt. Soc. Am. A 8, 1905–1913(1991). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited