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Image Denoising Based On Sparse Representation In A Probabilistic Framework
Mohamed EL-Sayed Waheed, Hassan Ahmad Khalil, Osama Farouk Hassan
Pages - 20 - 29     |    Revised - 01-06-2014     |    Published - 01-07-2014
Volume - 8   Issue - 3    |    Publication Date - July 2014  Table of Contents
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KEYWORDS
Sparse Representation, Image Denosing, Independent Component Analysis, Dictionary Learning.
ABSTRACT
Image denoising is an interesting inverse problem. By denoising we mean finding a clean image, given a noisy one. In this paper, we propose a novel image denoising technique based on the generalized k density model as an extension to the probabilistic framework for solving image denoising problem. The approach is based on using overcomplete basis dictionary for sparsely representing the image under interest. To learn the overcomplete basis, we used the generalized k density model based ICA. The learned dictionary used after that for denoising speech signals and other images. Experimental results confirm the effectiveness of the proposed method for image denoising. The comparison with other denoising methods is also made and it is shown that the proposed method produces the best denoising effect.
CITED BY (1)  
1 Xuan, S., & Han, Y. (2015). Improved extreme value weighted sparse representational image denoising with random perturbation. Journal of Electronic Imaging, 24(6), 063004-063004.
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Dr. Mohamed EL-Sayed Waheed
Faculty of Computers & Informatics / Department of Computer Science Suez Canal University Ismailia, Egypt - Egypt
Dr. Hassan Ahmad Khalil
Faculty of Science / Department of Mathematics Zagazig University Zagazig, Egypt - Egypt
Dr. Osama Farouk Hassan
Qassim university - Egypt
osamafarouk4@gmail.com