A divide-and-conquer stochastic alterable direction image denoising method

A novel image denoising method based on stochastic technique is proposed in this paper. The procedure is divided into two phases: the appropriate random sampling strategy is adopted to search for similar patches, then the original image is estimated by these patches. Specifically, in order to reduce the sampling rejection rate, the observed image is decomposed into different frequency bands by 2D wavelet transform, then the similar patches are collected by alterable direction Markov-Chain Monte Carlo (MCMC) sampling with a properly chosen rejection criterion. Rather than taking the weighted average of similar patches, we use two-directional non-local (TDNL) method in order to take full use of the similarity between similar patches collected. The simulation results show that the proposed method improves the efficiency of searching similar patches. Compared with the NLM and BM3D method, our approach has lower computational complexity, better performance in protecting image details and higher visual quality, respectively.