Image denoising using wavelet Bayesian network models
Jinn Ho, Wen-Liang Hwang · 2012
A number of techniques have been developed to deal with image denoising, which is regarded as the simplest inverse problem. In this paper, we propose an approach that constructs a Bayesian network from the wavelet coefficients of a single image such that different Bayesian networks can be obtained from different input images. Then, we utilize the maximum-a-posterior (MAP) estimator to derive the wavelet coefficients. Constructing a graphical model usually requires a large number of training images. However, we demonstrate that by using certain wavelet properties, namely, interscale data dependency, decorrelation between wavelet coefficients, and sparsity of the wavelet representation, a robust Bayesian network can be constructed from one image to resolve the denoising problem. Our experiment results show that, in terms of the peak-signal-to-noise-ratio (PSNR) performance, the proposed approach outperforms state-of-art algorithms on several images with various amounts of white Gaussian noise.