Image Noise Removal Using Rao-Blackwellized Particle Filter with Maximum Likelihood Estimation

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In this paper propose a noise removal method for reducing noise in digital images. An efficient Rao-Blackwellized Particle Filter (RBPF) with maximum likelihood Estimation approach is used for improving the learning stage of the image structural model and guiding the particles to the most appropriate direction. It increases the efficiency of particle transitions. The proposal distribution is computed by conditionally Gaussian state space models and Rao-Blackwellized particle filtering. The discrete state of operation is identified using the continuous measurements corrupted by Gaussian noise. The analytical structure of the model is computed by the distribution of the continuous states. The posterior distribution can be approximated with a recursive, stochastic mixture of Gaussians. Rao-Blackwellized particle filtering is a combination of a particle filter (PF) and a bank of Kalman filters. The distribution of the discrete states is computed by using Particle Filters and the distribution of the continuous states are computed by using a bank of Kalman filters. The Maximum likelihood Estimation method is used for noise distribution process. The RBPF with MLE is very effective in eliminating noise. RBPF with MLE is compared with particle filter, Markov Random Field particle filter and RBPF. In this paper different performance metrics are evaluated for this type of noise removal technique. The metrics are Mean Square error, Root Mean square error, Peak Signal to Noise Ratio, Normalized absolute Error, and Normalized Cross Correlation, Mean Absolute Error and Signal to Noise Ratio. Experimental results prove that RBPF with MLE outperforms for degraded medical images.

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ECG; MIT-BIH Database; Median Filter; FIR Filter; Gaussian Filter; Butterworth Filter

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A. Gulhane and A. S. Alvi, Noise Reduction of an Image by using Function Approximation Techniques, International Journal of Soft Computing and Engineering (IJSCE) Vol.2, Issue-1, March 2012.

S.S. Ieng, J.P. Tarel and P. Charbonnier, Modelling Non-Gaussian Noise For Robust Image Analysis, In proceeding of: VISAPP 2007: Proceedings of the Second International Conference on Computer Vision Theory and Applications, Barcelona, Spain, Vol. 1,March 8-11, 2007.

Jinn Ho and Wen-Liang Hwang, Wavelet Bayesian Network Image Denoising, IEEE Transactions On Image Processing, Vol. 22, no. 4, April 2013

P. Milanfar, A tour of modern image filtering: New Insights and methods, both practical and theoretical, IEEE Signal Process. Mag., vol. 30, no. 1, pp. 106–128, Jan. 2013.

Firas Ajil Jassim, Image Denoising Using Interquartile Range Filter with Local Averaging, International Journal of Soft Computing and Engineering (IJSCE), Vol. 2, Issue-6, January 2013.

R. Pushpavalli and G. Sivarajde, Image denoising using a Neuro hybrid fuzzy filtering technique, International Journal of Scientific & Technology Research Vol. 2, Issue 5, May 2013.

Idan Ram, Michael Elad and Israel Cohen, Image Denoising Using NL-Means Via Smooth Patch Ordering, Technion - Israel Institute of Technology, 2013.

Alamdeep Singh and Kuldeep Sharma, Enhancing Image Quality for Highly Noisy Images Using Gaussian and Bilateral Filter, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Vol. 2, Issue 9, September 2013.

R. Vanithamani and G. Umamaheswari, Speckle Reduction in Ultrasound Images Using Neighshrink and Bilateral Filtering, Journal of Computer Science 10 (4): 623-631, 2014

Hyuntaek .Bayesian ensemble learning for image denoising, Computer Vision and Pattern Recognition, 2013.

P. Krishnapriya and S. Sanjeeve Kumar, A Novel Approach to Noise Reduction for Impulse and Gaussian Noise, International Journal of Emerging Technology and Advanced Engineering, 2013.

Conte. F, Germani, A and Iannello, G. A Kalman Filter Approach for Denoising and Deblurring 3-D Microscopy Images. IEEE Transactions on Image Processing, 22(12), 5306 – 5321.

L. Jing, H. Chongzhao, and P. Vadakkepat, Process noise identification based particle filter: an efficient method to track highly manoeuvring targets. IET Signal Processing, 5(6), 2011, pp. 538 – 546.

B.S. Priys, A. Suruliandi. A, Empirical evaluation of image reconstruction techniques. Third International Conference on Computing Communication & Networking Technologies (ICCCNT), 1-8.

C. Tomasi, R. Manduchi, R, Bilateral filtering for gray and color images, in Proc. Int. Conf. Computer Vision, 2005, pp. 839–846.

M. Mahmoudi, G. Sapiro, Fast image and video de-noising via nonlocal means of similar neighborhoods. IEEE Signal Process. Lett., 12, 2005, pp. 839–842.

S. Lee, Digital image smoothing and the sigma filter. CVGIP, 24(2), 1983. pp. 255–269.

J. Polzehl, V. Spokoiny, Adaptive weights smoothing with applications to image restoration. J. Roy. Statist. Soc. B, 2, 2000, pp. 335–354.

Z. Azzabou, N. Paragios and F. Guichard, Random walks, constrained multiple hypothesis testing and image enhancement. In Proc.Eur. Conf. Computer Vision, 2006, pp. 379–390.

Noura Azzabou, Nikos Paragios and Frederic Guichard, Image Reconstruction using Particle Filters and Multiple Hypotheses Testing. IEEE Transactions on Image Processing, 19(5), 2010.

M. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 2002, pp. 174-188.

Anna Saro Vijendran and Bobby Lukose, Image Restoration using particle filters by improving the scale of texture with MRF. International Journal of Image Processing (IJIP), 6(5), 2012.

Doucet, S. Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 2000, pp. 197–208.

Giorgio Grisetti, Cyrill Stachniss and Wolfram Burgard, Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters. IEEE Transactions on Robotics, 23(1), 2007, pp.34-46.

I. Omer and M. Werman, Image specific feature similarities. In Proc. of European Conference on Computer Vision, 2, 2006, pp. 321–333.

T. A. Severini, Likelihood Methods in Statistics. Oxford University Press, 2001.

F. Alter, Y. Matsushita and X. Tang, An intensity similarity measure in low-light conditions. In Proc. of European Conference on Computer Vision, 2006, pp. 267–280.

Thomas Schon, Fredrik Gustafsson, and Per-Johan Nordlund, Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models. IEEE Transactions on Signal Processing, 53(7), 2005, pp. 2279-2289.

David Tornqvist, Thomas B. Schon, Rickard Karlsson and Fredrik Gustafsson. Particle Filter SLAM with High Dimensional Vehicle Model. Journal of Intelligent and Robotic Systems, 55(4-5), 2009, pp. 249-266.

N. Doucet, De Freitas, and Gordan,. editors. “Sequential Monte-Carlo Methods in Practice,” Springer Verlag, 2001.

M. West, Mixture models, Monte Carlo, Bayesian updating and dynamic models, Computing Science and Statistics 24, 1993, pp. 325–333.

G. Casella and C. P. Robert, Rao-Blackwellization of sampling schemes. Biometrika, 83 (1), 1996, pp. 81–94.

J.F.G. Freitas, Rao-Blackwellized particle filtering for fault diagnosis. Proceedings of the IEEE Aerospace Conference, no. 4, 2002, pp. 1767-1772.

Abdelkader Tami, Mokhtar Keche, Abdelaziz Ouamri, New Joint Blind Channel Estimation and Data Detection Through a Time Varying MIMO Channel, (2013) International Journal on Communications Antenna and Propagation (IRECAP), 3 (5), pp. 255-260.

Zahradnik, P., Vlcek, M., A computer program for designing notch FIR linear phase digital filters, (2012) International Review on Computers and Software (IRECOS), 7 (2), pp. 505-510.

Kuppusamy, P.G., Hemamalini, R.R., A VLSI based framework for iterative and adaptive based image filter for impulse noise removal, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 235-242.

Farahiah, N., Shahrizan, D., Ishak, S., Sarpinah, B., Jusoff, K., Fuzzy logic image enhancement, (2009) International Review on Computers and Software (IRECOS), 4 (4), pp. 440-446.

Wu, Z., An image filtering method based on improved particle swarm optimization algorithm, (2012) International Review on Computers and Software (IRECOS), 7 (3), pp. 1405-1411.

Xiao, F., Zhou, M., Geng, G., Edge detection and noise reduction for color image based on multi-scale, (2011) International Review on Computers and Software (IRECOS), 6 (6), pp. 1157-1162.


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