title: Wavelet Bayesian network image denoising.
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.
authors.affiliation: Institute of Information Science, Academia Sinica, Taipei 115, Taiwan. hjinn@iis.sinica.edu.tw
authors.forename: Jinn
authors.initials: J
authors.lastname: Ho
authors.forename: Wen-Liang
authors.initials: WL
authors.lastname: Hwang