A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise

TL;DR

This paper introduces NLSF-CNN, a novel two-step denoising method combining non-local filtering and CNNs to effectively remove salt and pepper noise, especially at high impulse levels.

Contribution

The paper presents a new non-local switching filter combined with CNN training for improved salt and pepper noise removal, outperforming existing methods.

Findings

NLSF-CNN achieves superior denoising performance.

Effective with limited training images.

Outperforms state-of-the-art algorithms.

Abstract

The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.