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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics


  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 7, Iss. 11 — Oct. 31, 2012

Joint filtering estimation of Stokes vector images based on a nonlocal means approach

Sylvain Faisan, Christian Heinrich, François Rousseau, Alex Lallement, and Jihad Zallat  »View Author Affiliations

JOSA A, Vol. 29, Issue 9, pp. 2028-2037 (2012)

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Conventional estimation techniques of Stokes images from observed radiance images through different polarization filters suffer from noise contamination that hampers correct interpretation or even leads to unphysical estimated signatures. This paper presents an efficient restoration technique based on nonlocal means, permitting accurate estimation of smoothly variable polarization signatures in the Stokes image while preserving sharp transitions. The method is assessed on simulated data as well as on real images.

© 2012 Optical Society of America

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(100.3190) Image processing : Inverse problems
(120.5410) Instrumentation, measurement, and metrology : Polarimetry

ToC Category:
Image Processing

Original Manuscript: April 18, 2012
Revised Manuscript: June 25, 2012
Manuscript Accepted: July 16, 2012
Published: August 31, 2012

Virtual Issues
Vol. 7, Iss. 11 Virtual Journal for Biomedical Optics

Sylvain Faisan, Christian Heinrich, François Rousseau, Alex Lallement, and Jihad Zallat, "Joint filtering estimation of Stokes vector images based on a nonlocal means approach," J. Opt. Soc. Am. A 29, 2028-2037 (2012)

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  1. R. A. Chipman, “Polarimetry,” in Handbook of Optics (McGraw-Hill, 1994), Chap. 22.
  2. J. Zallat, S. Ainouz, and M.-P. Stoll, “Optimal configurations for imaging polarimeters: impact of image noise and systematic errors,” J. Opt. A 8, 807–814 (2006). [CrossRef]
  3. J. Zallat and C. Heinrich, “Polarimetric data reduction: a Bayesian approach,” Opt. Express 15, 83–96 (2007). [CrossRef]
  4. J. Zallat, C. Heinrich, and M. Petremand, “A Bayesian approach for polarimetric data reduction: the Mueller imaging case,” Opt. Express 16, 7119–7133 (2008). [CrossRef]
  5. J. Valenzuela and J. Fessler, “Joint reconstruction of Stokes images from polarimetric measurements,” J. Opt. Soc. Am. A 26, 962–968 (2009). [CrossRef]
  6. G. Sfikas, C. Heinrich, J. Zallat, and C. Nikou, “Recovery of polarimetric Stokes images by spatial mixture models,” J. Opt. Soc. Am. A 28, 465–474 (2011). [CrossRef]
  7. A. Buades, B. Coll, and J. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul. 4, 490–530 (2005). [CrossRef]
  8. L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259–268 (1992). [CrossRef]
  9. R. Coifman and D. Donoho, “Translation-invariant denoising,” in Wavelets and Statistics (Springer Verlag, 1995), pp. 125–150.
  10. S. Kinderman, S. Osher, and P. Jones, “Deblurring and denoising of images by nonlocal functionals,” Multiscale Model. Simul. 4, 1091–1115 (2005). [CrossRef]
  11. G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7, 1005–1028 (2008). [CrossRef]
  12. M. Mignotte, “A non-local regularization strategy for image deconvolution,” Pattern Recogn. Lett. 29, 2206–2212 (2008). [CrossRef]
  13. F. Rousseau, “A non-local approach for image super-resolution using intermodality priors,” Medical Image Anal. 14, 594–605 (2010). [CrossRef]
  14. V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From local kernel to nonlocal multiple-model image denoising,” Int. J. Comput. Vis. 86, 1–32 (2010). [CrossRef]
  15. P. Coupé, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, “An optimized blockwise nonlocal means denoising filter for 3D magnetic resonance images,” IEEE Trans. Med. Imag. 27, 425–441 (2008). [CrossRef]
  16. T. Gasser, L. Sroka, and C. Steinmetz, “Residual variance and residual pattern in nonlinear regression,” Biometrika 73, 625–633 (1986). [CrossRef]
  17. B. Goossens, H. Luong, A. Pizurica, and W. Philips, “An improved non-local means algorithm for image denoising,” presented at the 2008 International Workshop on Local and Non-Local Approximation in Image Processing (LNLA2008), Lausanne, Switzerland, August23–242008.
  18. C.-A. Deledalle, V. Duval, and J. Salmon, “Non-local methods with shape-adaptive patches (NLM-SAP),” J. Math. Imaging Vision 43, 103–120 (2012). [CrossRef]
  19. J. Zallat, M. Torzynski, and A. Lallement, “Double-pass self-spectral-calibration of a polarization state analyzer,” Opt. Lett. 37, 401–403 (2012). [CrossRef]
  20. M. Hanson, “Invexity and the Kuhn–Tucker theorem,” J. Math. Anal. Appl. 236, 594–604 (1999). [CrossRef]

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