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Research Article on Image Denoising

Authors(2) :-Sangita Kulkarni, Prof. Anil Bavaskar

Now a days ,wavelet-based image denoising method ,which extends a recently emerged “geometrical” Bayesian framework. The new scheme combines three criteria of sparsity, clustering,and persistence which are united in a Bayesian network. We address the image denoising difficulty ,where zero-mean white and Gaussian additive noise is to be uninvolved from a given image . We employ the belief propagation (BP) algorithm which estimates a coefficient based on every one the coefficient of a picture as the maximum-a-posterior (MAP) estimator to derive the denoised wavelet coefficients. We show that if the network is spanning tree, the standard BP algorithm can perform MAP estimation efficiently. Our research consequences show that in condition of the peak-Signal- to- noise-ratio and perceptual quality. The planned approach out performs state-of -the-art algorithm on a several images, particularly in the textured regions with various amount of white Gaussian noise.
Sangita Kulkarni, Prof. Anil Bavaskar
Bayesian Network, Image Denoising, Wavelet Transform, Bayesian Restoration
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Publication Details
  Published in : Volume 3 | Issue 2 | January-February 2017
  Date of Publication : 2017-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 215-221
Manuscript Number : NCAEAS2350
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Sangita Kulkarni, Prof. Anil Bavaskar , "Research Article on Image Denoising ", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.215-221, January-February-2017
URL : http://ijsrst.com/NCAEAS2350