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

TL;DR

This paper introduces a novel image denoising algorithm for salt and pepper noise that leverages generative modeling and clustering of patches to improve noise detection and removal, resulting in superior visual quality and PSNR.

Contribution

It presents a new patch-based generative clustering approach combined with a non-local switching filter for effective salt and pepper noise removal.

Findings

Outperforms several state-of-the-art denoising algorithms.

Achieves higher peak signal-to-noise ratio scores.

Provides better visual quality in noisy images.

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, thus providing additional similarity information for noise repair and suppressing the interference of noise, abandoning those categories 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 obtain a better denoising effect, and provides a guideline for future denoising and repair methods.