Research Paper | Information Technology | India | Volume 5 Issue 5, May 2016
Image Denoising Using Standard BP Algorithm
T. Uma Mageswari
Abstract: From the perspective of the Bayesian approach, the denoising problem is essentially a prior probability modeling and estimation task. In this paper, we propose an approach that exploits a hidden Bayesian network, constructed from wavelet coefficients, to model the prior probability of the original image. Then, we use the belief propagation (BP) algorithm, which estimates a coefficient based on all the coefficients of an image, as the maximum-a-posterior (MAP) estimator to derive the denoised wavelet coefficients. We show that if the network is a spanning tree, the standard BP algorithm can perform MAP estimation efficiently. Our experiment results demonstrate that, in terms of the peak-signal-to-noise-ratio and perceptual quality, the proposed approach outperforms state-of-the-art algorithms on several images, particularly in the textured regions, with various amounts of white Gaussian noise.
Keywords: IMAGE DENOISING
Edition: Volume 5 Issue 5, May 2016,
Pages: 1242 - 1252
How to Cite this Article?
T. Uma Mageswari, "Image Denoising Using Standard BP Algorithm", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=NOV163209, Volume 5 Issue 5, May 2016, 1242 - 1252, #ijsrnet
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