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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
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Canny, Edge Sharpener, Edge Detection
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.
CITED BY (4)  
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 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
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.
A. Doucet, J. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice.New York:Springer-Verlag, 2001.
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.
A. Efros and T. Leung, “Texture synthesis by non-parametric sampling,” in Proc. Int. Conf.Computer Vision, 1999, pp. 1033–1038.
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.
B. Smolka and K. Wojciechowski, “Random walk approach to image enhancement,” Signal Process., vol. 81, pp. 465–482, 2001.
C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. Int. Conf.Computer Vision, 1998, pp. 839–846.
D. Geman, “Random fields and inverse problems in imaging,” in Lecture Notes in Mathematics,vol. 1427, pp. 113–193. Springer–Verlag, 1991.
G. Kitagawa, “Monte carlo filter and smoother for non-Gaussian nonlinear state space models,”J. Comput.Graph.Statist., vol. 5, pp.1–25, 1996.
J. Carpenter, P. Clifford, and P. Fearnhead, “Improved particle filter for nonlinear problems,” Proc.Inst. Elect. Eng., Radar, Sonar, Navig., 1999.
J. Huang and D. Mumford, “Statistics of natural images and models,” in Proc. IEEE Int. Conf.Computer Vision and PatternRecognition, 1999, pp. 541–547.
J. Polzehl and V. Spokoiny, “Adaptive weights smoothing with applications to image restoration,”J. Roy.Statist.Soc.B, vol. 62, pp.335–354, 2000.
J. S. D. Bonet. Multiresolution sampling procedure for analysis and synthesis of texture images. In SIGGRAPH ’97, pages 361–368,1997.
J. S. Liu and R. Chen, “Sequential Monte Carlo methods for dynamical systems,” J. Amer.Statist.Assoc., vol. 93, pp. 1032–1044,1998.
John Moussouris, “Gibbs and Markov random systems with constraints,” Journal of Statistical Physics, vol. 10, no. 1, pp. 11–33, 1974.
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.
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.
N. Azzabou, N. Paragios, and F. Guichard, “Random walks, constrained multiple hypothesis testing and image enhancement,” inProc.Eur. Conf. ComputerVision, 2006, pp. 379–390.
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.
P. Del Moral, “Non-linear filtering: Interacting particle solution,” Markov Processes Related Fields,vol. 2, no. 4, pp. 555–580.
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.
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.
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.
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.
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.
S. Lee, “Digital image smoothing and the sigma filter,” CVGIP, vol. 24, no. 2, pp. 255–269, Nov.1983.
S.Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, pp. 674–693,1989.
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.
Dr. Anna Saro Vijendran
S.N.R Sons College - India
Mr. Bobby Lukose
Hindusthan College of Arts & Science - India

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