A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

TL;DR

This paper introduces SeConvNet, a deep CNN with selective convolutional blocks, designed specifically for salt-and-pepper noise removal, demonstrating superior performance over existing methods in both quantitative and visual assessments.

Contribution

The paper presents a novel deep CNN architecture with selective convolutional blocks tailored for salt-and-pepper noise reduction, addressing a gap in CNN applications for this noise type.

Findings

SeConvNet outperforms state-of-the-art methods at high noise densities.

The model effectively restores images corrupted by salt-and-pepper noise.

Experimental results show superior quantitative and visual quality.

Abstract

In recent years, there has been an unprecedented upsurge in applying deep learning approaches, specifically convolutional neural networks (CNNs), to solve image denoising problems, owing to their superior performance. However, CNNs mostly rely on Gaussian noise, and there is a conspicuous lack of exploiting CNNs for salt-and-pepper (SAP) noise reduction. In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images. To meet this objective, we introduce a new selective convolutional (SeConv) block. SeConvNet is compared to state-of-the-art SAP denoising methods using extensive experiments on various common datasets. The results illustrate that the proposed SeConvNet model effectively restores images corrupted by SAP noise and surpasses all its counterparts at both quantitative criteria and visual effects, especially at high and very high noise densities.