A salt and pepper noise image denoising method based on the generative classification

Bo Fu, Xiaoyang Zhao, Chuanming Song, Ximing Li, Xiang-Hai Wang

Abstract: In this paper, an image denoising algorithm is proposed for salt and pepper noise. First, a generative model is built on a patch as a basic unit and then the algorithm locates the image noise within that patch in order to better describe the patch and obtain better subsequent clustering. Second, the algorithm classifies patches using a generative clustering method, which provides additional similarity information for noise repairing, suppresses the interference of noise and abandons those classes that consist of a smaller number of patches. Finally, the algorithm builds a non-local switching filter to remove the salt and pepper noise. Simulation results show that the proposed algorithm effectively denoises salt and pepper noise of various densities. It obtains a better visual quality and higher peak signal-to-noise ratio score than several state-of-the-art algorithms. In short, our algorithm uses a noisy patch as the basic unit, a patch clustering method to optimize the repair data set as well as obtains a better denoising effect, and provides a guideline for future denoising and repair methods.