Nonlocal structured nonparametric Bayesian dictionary learning for image denoising

In this work, we propose a sparse Bayesian dictionary learning framework with structure prior which connects nonlocal self-similarity and sparse Bayesian dictionary learning. A Gamma-Gaussian prior is used to impose sparsity and a nonlocal beta process is utilized to introduce the nonlocal self-similarity as a structure prior for image denoising. Unlike most of the existing image denoising methods, our proposed method does not need to know noise variance in advance like an unsupervised learning. The experimental results demonstrate the effectiveness of our proposed model. It can be observed that the undesirable artifacts can be suppressed significantly and the structure of image can be preserved effectively.