Salt-and-Pepper Noise Removal Based on Sparse Signal Processing

TL;DR

This paper introduces a novel salt-and-pepper noise removal technique based on sparse signal processing, transforming denoising into a sparse reconstruction problem to improve image quality and handle missing data effectively.

Contribution

The paper presents a new sparse signal processing approach for salt-and-pepper noise removal, offering better preservation of image details and versatility in reconstructing missing samples.

Findings

Outperforms existing methods in PSNR and visual quality

Effectively reconstructs missing samples in erasure channels

Preserves image opacity better than traditional filters

Abstract

In this paper, we propose a new method for Salt-and-Pepper noise removal from images. Whereas most of the existing methods are based on Ordered Statistics filters, our method is based on the growing theory of Sparse Signal Processing. In other words, we convert the problem of denoising into a sparse signal reconstruction problem which can be dealt with the corresponding techniques. As a result, the output image of our method is preserved from the undesirable opacity which is a disadvantage of most of the other methods. We also introduce an efficient reconstruction algorithm which will be used in our method. Simulation results indicate that our method outperforms the other best-known methods both in term of PSNR and visual criterion. Furthermore, our method can be easily used for reconstruction of missing samples in erasure channels.