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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 27, Iss. 5 — May. 1, 2010
  • pp: 1091–1099

Recursive framework for joint inpainting and de-noising of photographic films

G. R.K.S. Subrahmanyam, A. N. Rajagopalan, and R. Aravind  »View Author Affiliations


JOSA A, Vol. 27, Issue 5, pp. 1091-1099 (2010)
http://dx.doi.org/10.1364/JOSAA.27.001091


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Abstract

We address the problem of inpainting noisy photographs. We present a recursive image recovery scheme based on the unscented Kalman filter (UKF) to simultaneously inpaint identified damaged portions in an image and suppress film-grain noise. Inpainting of the missing observations is guided by a mask-dependent reconstruction of the image edges. Prediction within the UKF is based on a discontinuity-adaptive Markov random field prior that attempts to preserve edges while achieving noise reduction in uniform regions. We demonstrate the capability of the proposed method with many examples.

© 2010 Optical Society of America

OCIS Codes
(100.2980) Image processing : Image enhancement
(100.3020) Image processing : Image reconstruction-restoration

ToC Category:
Image Processing

History
Original Manuscript: August 18, 2009
Revised Manuscript: February 1, 2010
Manuscript Accepted: February 1, 2010
Published: April 16, 2010

Citation
G. R. K. S. Subrahmanyam, A. N. Rajagopalan, and R. Aravind, "Recursive framework for joint inpainting and de-noising of photographic films," J. Opt. Soc. Am. A 27, 1091-1099 (2010)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-5-1091


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References

  1. M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, “Image inpainting,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (ACM Press, 2000), pp. 417–424.
  2. T. F. Chan and J. Shen, “Mathematical models of local non-texture inpainting,” SIAM J. Appl. Math. 62, 1019–1043 (2001).
  3. B. R. Hunt, “Bayesian methods in nonlinear digital image restoration,” IEEE Trans. Comput. 26, 219–229 (1977). [CrossRef]
  4. A. Stefano, B. Collis, and P. White, “Synthesising and reducing film grain,” J. Visual Commun. Image Represent 17, 163–182 (2006). [CrossRef]
  5. A. C. Kokaram, R. D. Morris, W. J. Fitzgerald, and P. J. W. Rayner, “Interpolation of missing data in image sequences,” IEEE Trans. Image Process. 11, 1509–1519 (1995). [CrossRef]
  6. J. Jia and C. Tang, “Image repairing: Robust image synthesis by adaptive ND tensor voting,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 643–650.
  7. A. Telea, “An image inpainting technique based on the fast marching method,” J. Graph. Tools 9, 25–36 (2004).
  8. A. Rares, M. J. T. Reinders, and J. Biemond, “Edge-based image restoration,” IEEE Trans. Image Process. 14, 1454–1468 (2005). [CrossRef] [PubMed]
  9. N. Komodakis and G. Tziritas, “Image completion using global optimization,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 442–452.
  10. J. Jia, Y. Tai, T. Wu, and C. Tang, “Video repairing under variable illumination using cyclic motions,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 832–839 (2006). [CrossRef] [PubMed]
  11. Y. Matsushita, E. Ofek, W. Ge, X. Tang, and H. Shum, “Full-frame video stabilization with motion inpainting,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1150–1164 (2006). [CrossRef] [PubMed]
  12. K. A. Patwardhan, G. Sapiro, and M. Bertalmio, “Video inpainting under constrained camera motion,” IEEE Trans. Image Process. 16, 1200–1212 (2007). [CrossRef]
  13. C. Ballester, M. Bertalmio, V. Caselles, L. Garrido, A. Marques, and F. Ranchin, “An inpainting-based deinterlacing method,” IEEE Trans. Image Process. 16, 2476–2491 (2007). [CrossRef] [PubMed]
  14. F. Naderi and A. A. Sawchuk, “Estimation of images degraded by film-grain noise,” Appl. Opt. 20, 1228–1237 (1978). [CrossRef]
  15. A. M. Tekalp and G. Pavlovic, “Restoration in the presence of multiplicative noise with application to scanned photographic images,” IEEE Trans. Signal Process. 39, 2132–2136 (1991). [CrossRef]
  16. A. D. Stefano, P. R. White, and W. B. Collis, “Film grain reduction on colour images using undecimated wavelet transform,” Image Vision Comput. 22, 873–882 (2004). [CrossRef]
  17. S. Ibrahim Sadhar and A. N. Rajagopalan, “Image estimation in film-grain noise,” IEEE Signal Process. Lett. 12, 238–241 (2005). [CrossRef]
  18. G. R. K. S. Subrahmanyam, A. N. Rajagopalan, and R. Aravind, “Importance-sampling-based unscented Kalman filter for film-grain noise removal,” IEEE Multimedia 15, 52–63 (2008). [CrossRef]
  19. A. C. Kokaram and S. J. Godsill, “MCMC for joint noise reduction and missing data treatment in degraded video,” IEEE Trans. Signal Process. 50, 189–205 (2002). [CrossRef]
  20. A. C. Kokaram, “On missing data treatment for degraded video and film archives: A survey and a new Bayesian approach,” IEEE Trans. Image Process. 13, 397–415 (2004). [CrossRef] [PubMed]
  21. C. A. Z. Barcelos and M. A. Batista, “Image restoration using digital inpainting and noise removal,” Image Vision Comput. 25, 61–69 (2007). [CrossRef]
  22. P. Favaro and S. Soatto, “Seeing beyond occlusions (and other marvels of a finite lens aperture),” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 579–586.
  23. S. W. Hasinoff and K. N. Kutulakos, “A layer-based restoration framework for variable-aperture photography,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1–8.
  24. S. J. Julier and J. K. Uhlmann, “A new extension of the Kalman filter to nonlinear systems,” in Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls: Signal Processing, Sensor Fusion, and Target Recognition VI, Orlando, Florida (1997), Vol. 3068, pp. 182–183.
  25. R. van der Merwe, J. F. G. de Freitas, D. Doucet, and E. A. Wan, “The unscented particle filter,” Technical Report CUED/F-INFENG/TR 380, Cambridge University Engineering Department (2000).
  26. S. J. Julier and J. K. Uhlmann, “A general method for approximating nonlinear transformations of probability distributions,” Tech. Rep. RRG, Dept. of Engineering Science (University of Oxford, 1996).
  27. S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer, 1995).
  28. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984). [CrossRef]
  29. D. J. C. Mackay, “Introduction to Monte Carlo methods,” in Learning in Graphical Models, NATO Science Series, M.I.Jordan, ed. (Kluwer, 1998), pp. 175–204.
  30. A. K. Jain, “Advances in mathematical models for image processing,” IEEE Photon. Technol. Lett. 69, 502–528 (1981).
  31. D. Corrigan, N. Harte, and A. Kokaram, “Automatic segmentation of torn frames using the graph cuts technique,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2007), pp. I-557–I-560.

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