Image denoising based on a statistical model for wavelet coefficients

In this paper, we propose a new statistical model for the relationship of wavelet coefficients and its application to image denoising. The magnitude of a wavelet coefficient usually shows high correlations with the nearby ones. This property has been exploited in many wavelet-based image processing techniques. However, conventional works consider only the local neighborhood of a coefficient when inferring its hidden state. Consequently, the image context is not faithfully reflected and thus there are sometimes visually annoying artifacts. We attempt to alleviate this problem by developing a new statistical model for the random field that is consisted of hidden variables of the overall band and thus includes global relationship of wavelet coefficients. In this model, the image context is encoded by the relations of hidden states, and the state plane is efficiently inferred by the sum-product algorithm. In the experiment, the proposed model is incorporated with the state-of-the-art denoising algorithm, namely BLS-GSM (Bayes Least Square - Gaussian Scale Mixture). The results show that the proposed algorithm suppresses many annoying artifacts that exist in the conventional denoising methods, and thus improves the subjective quality.