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Issue No.02 - April-June (2008 vol.15)
pp: 52-63
G.R.K. Sai Subrahmanyam , Indian Institute of Technology Madras
A.N. Rajagopalan , Indian Institute of Technology Madras
Rangarajan Aravind , Indian Institute of Technology Madras
Photographic film contains film-grain noise that translates to multiplicative, non-Gaussian noise in the exposure domain. A method based on the unscented Kalman filter can suppress this noise while simultaneously preserving edge information.
film-grain noise, unscented Kalman filter, Markov random field (MRF), discontinuity adaptive MRFs, importance sampling
G.R.K. Sai Subrahmanyam, A.N. Rajagopalan, Rangarajan Aravind, "Importance Sampling-Based Unscented Kalman Filter for Film-Grain Noise Removal", IEEE MultiMedia, vol.15, no. 2, pp. 52-63, April-June 2008, doi:10.1109/MMUL.2008.32
1. D. Zou et al., "Data Hiding in Film Grain,," Proc. 5th Int'l Workshop Digital Watermarking, LNCS 4283, Springer, 2006, pp. 197-211.
2. T.M. Moldovan, S. Roth, and M.J. Black, "Denoising Archival Films Using a Learned Bayesian Model,," Proc. IEEE Int'l Conf. Image Processing, 2006, pp. 2641-2644, icip2006.pdf.
3. H.C. Andrews and B.R. Hunt, Digital Image Restoration, Prentice-Hall, 1977.
4. A.M. Tekalp and G. Pavlovic, "Restoration in the Presence of Multiplicative Noise with Application to Scanned Photographic Images,," IEEE Trans. Signal Processing, vol. 39, 1991, pp. 2132-2136.
5. S.J. Julier and J.K. Uhlmann, "A New Extension of the Kalman Filter to Nonlinear Systems,," SPIE AeroSense Symp, 1997, pp. 182-193,˜welch/kalman/ media/pdfJulier1997_SPIE_KF.pdf.
6. R. van der Merwe et al., The Unscented Particle Filter, tech. Report CUED/F-INFENG/TR 380, Engineering Dept., Cambridge Univ., 2000.
7. S.J. Julier and J.K. Uhlmann, A General Method for Approximating Nonlinear Transformations of Probability Distributions, tech. report, RRG, Dept. of Engineering Science, Univ. of Oxford, 1996.
8. H. Kaufman et al., "Estimation and Identification of Two-Dimensional Images,," IEEE Trans. Automatic Control, vol. 28, no. 7, 1983, pp. 745-756.
9. F.C. Jeng and J.W. Woods, "Inhomogeneous Gaussian Image Models for Estimation and Restoration,," IEEE Trans. Acoustics Speech and Signal Proc, vol. 36, no. 8, 1988, pp. 1305-1312.
10. S.R. Kadaba, S.B. Gelfand, and R.L. Kashyap, "Recursive Estimation of Images Using Non-Gaussian Autoregressive Models,," IEEE Trans. Image Processing, vol. 7, no. 10, 1998, pp. 1439-1452.
11. F.C. Jeng and J.W. Woods, "Compound Gauss-Markov Random Fields for Image Estimation,," IEEE Trans. Signal Processing, vol. 39, no. 3, 1991, pp. 683-697.
12. M. Ceccarelli, "Fast Edge-Preserving Picture Recovery by Finite Markov Random Fields,," F. Roil, and S. Vitulano eds. , Proc. Int'l Conf. Image Analysis and Processing, Springer Verlag, 2005, pp. 277-286.
13. S.Z. Li, Markov Random Field Modeling in Computer Vision, Springer Verlag, 1995.
14. S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 11, 1984, pp. 721-741.
15. R.G. Aykroyd, "Bayesian Estimation for Homogeneous and Inhomogeneous Gaussian Random Fields,," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, 1998, pp. 533-539.
16. D.J.C. Mackay, "Introduction to Monte Carlo Methods,," Learning in Graphical Models, M.I. Jordan ed. NATO Science Series, Kluwer Academic Press, 1998, pp. 175-204.
17. S.I. Sadhar, and A.N. Rajagopalan, "Image Recovery Under Nonlinear and Non-Gaussian Degradations,," J. Optical Society of America-A, vol. 22, 2005, pp. 604-615.
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