A stochastic image denoising method based on adaptive patch-size

A new stochastic nonlocal denoising method based on adaptive patch-size is presented. The quality of restored image is improved by choosing the optimal nonlocal similar patch-size for each site of image individually. The method contains two phase. The first phase is to search the similar patches base on adaptive patch-size. The second phase is to design the denoising algorithm by making use of similar image patches obtained in the first step. The multiple clusters of similar patches for each pixel point are searched by using Markov-chain Monte Carlo sampling many times. Following, we adjust the patch-size according to the consistency of multiple clusters. This processing is repeated until we obtain the optimal patch-size and corresponding optimal patch cluster. We get the estimation of noise-free patch cluster by employing modified two-directional non-local method. Furthermore, the denoised image is obtained by using the method of superposition approach. The theoretical analysis and simulation results show that the method is feasible and effective.

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