Image Denoising Using Standard BP Algorithm
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
How to Cite?: T. Uma Mageswari, "Image Denoising Using Standard BP Algorithm", Volume 5 Issue 5, May 2016, International Journal of Science and Research (IJSR), Pages: 1242-1252, https://www.ijsr.net/getabstract.php?paperid=NOV163209, DOI: https://dx.doi.org/10.21275/NOV163209