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Image Restoration Using Particle Filters By Improving The Scale Of Texture With MRF
Anna Saro Vijendran, Bobby Lukose
Pages - 306 - 316 | Revised - 15-09-2012 | Published - 24-10-2012
Published in International Journal of Image Processing (IJIP)
MORE INFORMATION
KEYWORDS
Canny, Edge Sharpener, Edge Detection
ABSTRACT
Traditional techniques are based on restoring image values based on local smoothness constraints within fixed bandwidth windows where image structure is not considered. Common problem for such methods is how to choose the most appropriate bandwidth and the most suitable set of neighboring pixels to guide the reconstruction process. The present work proposes a denoising technique based on particle filtering using MRF (Markov Random Field). It is an automatic technique to capture the scale of texture. The contribution of our method is the selection of an appropriate window in the image domain. For this we first construct a set containing all occurrences then the conditional pdf can be estimated with a histogram of all center pixel values. Particle evolution is controlled by the image structure leading to a filtering window adapted to the image content. Our method explores multiple neighbors’ sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypothesis according to its relevance and its contribution in the denoising process.
1 | Lukose, B., & Vijendran, A. S. (2014). Image Noise Removal Using Rao-Blackwellized Particle Filter with Maximum Likelihood Estimation. International Review on Computers and Software (IRECOS), 9(5), 784-792. |
2 | Vijendran, A. S., & Lukose, B. (2013). An Improved Image Denoising Technique for Digital Mobile Camera Images. International Journal of Advanced Computer Research, 3(3), 184. |
3 | Vijendran, A. S., Lukose, B., & Head, D. Fast and Efficient Method for Image Denoising. |
4 | SaroVijendran, A., & Lukose, B. Removal of Gaussian Noise Using Rao-Blackwellised Particle Filters. |
1 | M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesiantracking,” IEEE Trans. Signal Process., vol. 50, pp. 174–188, 2002. |
2 | N. Azzabou, N. Paragios, and F. Guichard, “Random walks, constrained multiple hypothesis testing and image enhancement,” inProc.Eur. Conf. ComputerVision, 2006, pp. 379–390. |
3 | N. Azzabou, N. Paragios, F. Guichard, and F. Cao, “Variable bandwidth image denoising using image-based noise models,” in Proc. IEEE Int.Conf. Computer Vision and Pattern Recognition,2007, pp. 1–7. |
4 | J. S. D. Bonet. Multiresolution sampling procedure for analysis and synthesis of texture images. In SIGGRAPH ’97, pages 361–368,1997. |
5 | A. Buades, B. Coll, and J.-M.Morel, “A non-local algorithm for image denoising,” in Proc. IEEE Int.Conf. Computer Vision and Pattern Recognition, 2005, pp. 60–65. |
6 | J. Carpenter, P. Clifford, and P. Fearnhead, “Improved particle filter for nonlinear problems,” Proc.Inst. Elect. Eng., Radar, Sonar, Navig., 1999. |
7 | T. Clapp and S. Godsill, “Improvement strategies for Monte Carlo particle filters,” in Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G.deFreitas, and N. J. Gordon, Eds. New York:Springer-Verlag, 2001. |
8 | P. Del Moral, “Non-linear filtering: Interacting particle solution,” Markov Processes Related Fields,vol. 2, no. 4, pp. 555–580. |
9 | A. Doucet, J. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice.New York:Springer-Verlag, 2001. |
10 | A. Doucet, N. Gordon, and V. Krishnamurthy, “Particle filters for state estimation of jump Markov linear systems,” IEEE Trans.Signal Processing, vol. 49, pp. 613–624, Mar. 2001. |
11 | A. Efros and T. Leung, “Texture synthesis by non-parametric sampling,” in Proc. Int. Conf.Computer Vision, 1999, pp. 1033–1038. |
12 | D. Geman, “Random fields and inverse problems in imaging,” in Lecture Notes in Mathematics,vol. 1427, pp. 113–193. Springer–Verlag, 1991. |
13 | S. Geman and C. Graffigne, “Markov random field image models and their applications to computer vision,” Proceedings of theInternational Congress of Mathematicians, pp. 1496–1517,1986. |
14 | R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst., Man, Cybern., vol.SMC-6, pp. 610–621, 1973. |
15 | R. E. Helmick, D. Blair, and S. A. Hoffman, “Fixed-interval smoothing for Markovian switching systems,” IEEE Trans. Inform.Theory, vol. 41, pp.1845–1855, Nov. 1995. |
16 | J. Huang and D. Mumford, “Statistics of natural images and models,” in Proc. IEEE Int. Conf.Computer Vision and PatternRecognition, 1999, pp. 541–547. |
17 | John Moussouris, “Gibbs and Markov random systems with constraints,” Journal of Statistical Physics, vol. 10, no. 1, pp. 11–33, 1974. |
18 | G. Kitagawa, “Monte carlo filter and smoother for non-Gaussian nonlinear state space models,”J. Comput.Graph.Statist., vol. 5, pp.1–25, 1996. |
19 | A. Lee, K. Pedersen, and D. Mumford, “The nonlinear statistics of high-contrast patches in natural images,” Int. J. Comput.Vis., pp.83–103, 2003. |
20 | S. Lee, “Digital image smoothing and the sigma filter,” CVGIP, vol. 24, no. 2, pp. 255–269, Nov.1983. |
21 | J. S. Liu and R. Chen, “Sequential Monte Carlo methods for dynamical systems,” J. Amer.Statist.Assoc., vol. 93, pp. 1032–1044,1998. |
22 | M. Mahmoudi and G. Sapiro, “Fast image and video denoising via nonlocal means of similar neighborhoods,” IEEE SignalProcess.Lett., vol. 12, pp. 839–842, 2005. |
23 | S.Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, pp. 674–693,1989. |
24 | J. Polzehl and V. Spokoiny, “Adaptive weights smoothing with applications to image restoration,”J. Roy.Statist.Soc.B, vol. 62, pp.335–354, 2000. |
25 | Richard C. Dubes and Anil K. Jain, “Random field models in image analysis,” Journal of Applied Statis-tics, vol. 16, no. 2, pp. 131–164, 1989. |
26 | B. Smolka and K. Wojciechowski, “Random walk approach to image enhancement,” Signal Process., vol. 81, pp. 465–482, 2001. |
27 | R. L. Streit and R. F. Barrett, “Frequency line tracking using hidden Markov models,” IEEE Trans.Acoust., Speech, Signal Processing, vol. 38, pp. 586–598, Apr. 1990. 188 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 2,FEBRUARY 2002. |
28 | C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. Int. Conf.Computer Vision, 1998, pp. 839–846. |
Dr. Anna Saro Vijendran
S.N.R Sons College - India
saroviji@rediffmail.com
Mr. Bobby Lukose
Hindusthan College of Arts & Science - India