Removing noise from the original signal is still a challenging problem for researchers. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Reduction of noise is essential especially in the field of image processing. Several researchers are continuously working in this direction and provide some good insights, but still there are lot of scope in this field. Noise mixed with image is harmful for image processing. In this dissertation we proposed an efficient Multithresholding  approach for reducing noise and blur parameters. 


Partial Differential Equation, BER, Signal to Noise Ratio, Peak Signal To Noise Ratio, Image Restoration

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W. P. Ding and F. Wu, “Adaptive directional lifting based wavelet transform for image coding,” IEEE Trans. Image Processing, vol. 16, no. 2, pp. 416–684, 2007

Y. Liu and K. N. Ngan, “Weighted adaptive lifting-based wavelet transform,”IEEE Trans. Image Processing, vol. 17, no. 4, pp. 500–511, 2008.

X. T. Wang, G. M. Shi, and Y. Niu, “Image denoising based on improved adaptive directional lifting wavelet transform,” in International Conference on Signal Processing, 2008, vol. 2, pp. 1112–1116.

X. T. Wang, G. M. Shi, Y. Niu, and L. Zhang, “Robust adaptive directional lifting wavelet transform for image denoising,” IET Image Process (Accepted), 2009.

G. Y. Chen, B. Kegl, “Image denoising with complex ridgelets,”Pattern Recognition, vol. 40, 2007,pp. 578-585,.

Z. Liu, H. Xu, “Image Denoising with Nonsubsampled Wavelet-Based Contourlet Transform,” Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008, pp. 301-305.

J. R. Sveinsson, Z. Semar, J. A. Benediktsson, “Speckle Reduction of SAR Images in the Bandlet Domain,” IEEE International Geoscience and Remote Sensing Symposium, 2008, pp. 1158-1161.

B. Fisch and E.L. Schowart, "Iearning an Integral Equation Approximation to Nonlinear Anisotropic Diffusion in Image processing", Dept. cognitive and Neural Systems Boston University.

Perona. P and Ma[ik. J, "Sca[e-space and edge detection using anisotropic diffusion," in Proceeding of IEEE Computer Society workshop on Computer Vision, 1987, pp. 16-27.

Y. You, W. Xu, A. Tannenbaum and M. Kaveh, "Behaviora[ analysis of an isotropic diffusion in image processing", IEEE Trans. Image Processing, vol. 5, 1996, pp. 1539-1553.

D. Strong and T. Chan, "Edge-preserving and scale-dependent properties of total variation regularization", Inverse Problems, vol. 19, 2003, pp. 165-187.

Sujata, R. B. Dubey,R. Dhiman T. J. Singh Chugh, “An Evaluation of Two Mammography Segmentation Techniques”, International Journal of Advanced Computer Research (IJACR) Volume-2 Number-4 Issue-7 December-2012.

Meenakshi, R. B. Dubey, “Vehicle License Plate Recognition System”, International Journal of Advanced Computer Research (IJACR) Volume-2 Number-4 Issue-7 December-2012.

Meenal Jain, Sumit Sharma, Ravi Mohan Sairam, “Effect of Blur and Noise on Image Denoising based on PDE”, International Journal of Advanced Computer Research (IJACR) Volume-3 Number-1 Issue-8 March-2013.

Meenal Jain, Sumit Sharma, Ravi Mohan Sairam, “Result Analysis of Blur and Noise on Image Denoising based on PDE”, International Journal of Advanced Computer Research (IJACR) Volume-2 Number-4 Issue-7 December-2012.

David L.Donoho and Iain M.Johnstone, 1994, “Ideal spatial adaptation via waveletshrinkage”, Biometrika, Vol.81, pp.425-455.

Raghuveer M.Rao and Ajit S.Bobaradikar, 1998, “Wavelet transforms: Introduction to theory and applications”, Addison Wesley Longman Inc.pp.183-189.

Saeid Fazli, Hamed Bouzari and Hamed Moradi pour, “Complex PDE Image Denoising Based on Particle Swarm Optimization”, 2010 International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT).

Changsheng Lang, Guangzheng Li , Jianhong Li, Xiujuan Zhao,” Combined Transform Image Denoising based on Morphological Component Analysis”, IEEE 2011.

Kehua Su, Hongbo Fu, Bo Du, Hong Cheng, Haofeng Wang and Dengyi Zhang, “Image Denoising based on Learning Over-complete Dictionary”, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012).

Guo-Duo Zhang, Xu-Hong Yang, Hang Xu, DongQing Lu, Yong-Xiao Liu, “Image Denoising Based On Support Vector Machine”, IEEE 2012.

Jia Liu, Caicheng Shi and Meiguo Gao, “Image Denoising Based on BEMD and PDE”, IEEE 2011.

R. G. Baraniuk, “Optimal tree approximation with wavelets,” in Proc. SPIE Tech. Conf.Wavelet Applications Signal Processing VII, vol. 3813, Denver, CO, 1999, pp. 196-207.

J. Lu, J. B.Weaver, D.M. Healy, and Y. Xu, “Noise reduction with multiscale edge representation and perceptual criteria,” in Proc. IEEE-SP Int. Symp. TimeFrequency and Time-Scale Analysis, Victoria, BC, Oct. 1992, pp. 555–558.

D. L. Donoho, “CART and best-ortho-basis: A connection,” Ann. Statist., pp. 1870–1911, 1997.

. R. W. Buccigrossi, and E. P. Simoncelli, ‘Image compression via joint statistical characterization in the wavelet domain’, IEEE Image Process., Vol. 8, No 12, Dec.1999, pp. 1688-1701.

. J. K. Romberg, H. Choi, and R. G. Baraniuk, “Bayesian tree-structured image modeling using wavelet-domain hidden Markov models”, IEEE Image Process., Vol. 10, No 7, Jul. 2001, pp. 1056-1068.

. H. A. Chipman, E. D. Kolaczyk, and R. E. McCulloch: “Adaptive Bayesian wavelet shrinkage”, J. Amer. Stat. Assoc., Vol. 92, No 440, Dec. 1997, pp. 1413-1421.

. P. Moulin and J. Liu, “Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors”, IEEE Infor. Theory, Vol. 45, No 3, Apr. 1999, pp. 909-919.

M. K. Mihcak, I. Kozintsev, K. Ramchandran, and P. Moulin, "Low-Complexity Image Denoising Based on Statistical Modeling of Wavelet Coefficients," IEEE Signal Processing Lett. (to appear).

Portilla, J., Strela, V., Wainwright, M., Simoncelli, E.P., “Image Denoising using Gaussian Scale Mixtures in the Wavelet Domain”, TR2002-831, Computer Science Dept, New York University. 2002.

V. Strela, J. Portilla, and E. P. Simoncelli, “Image denoising via a local Gaussian scale mixture model in the wavelet domain,” in Proc. SPIE 45th Annu. Meeting, San Diego, CA, Aug. 2000.

E. P. Simoncelli and E. Adelson, “Noise removal via Bayesian wavelet coring,” in Proc. IEEE International Conference on Image Processing, Lausanne, Switzerland, September 1996, pp. 279–382.

S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Processing, vol. 9, pp. 1522–1531, Sept. 2000.

J. Romberg, H. Choi and R. G. Baraniuk, "Bayesian wavelet domain image modeling using hidden Markov models," IEEE Transactions on Image Processing, vol. 10, pp. 1056-1068, July 2001.

M. Malfait and D. Roose, “Wavelet based image denoising using a Markov Random Field a priori model,” IEEE Transactions on Image Processing, vol. 6, no. 4, pp. 549–565, 1997.

M. Jansen and A. Bulthel, “Empirical bayes approach to improve wavelet thresholding for image noise reduction,” Journal of the American Statistical Association, vol. 96, no. 454, pp. 629–639, June 2001.

A. Jung, “An introduction to a new data analysis tool: Independent Component Analysis”, Proceedings of Workshop GK "Nonlinearity" - Regensburg, Oct. 2001.

A. Hyvärinen, E. Oja, P. Hoyer, and J. Hurri, “Image feature extraction by sparse coding and independent component analysis”, In Proc. Int. Conf. on Pattern Recognition (ICPR'98), pp. 1268-1273, Brisbane, Australia, 1998.

Nason, G.P., “Fast cross-validatory choice of wavelet smoothness, primary resolution and threshold in wavelet shrinkage using the KovacSilverman algorithm”, Technical report, Department of Mathematics, University of Bristol, United Kingdom.


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