Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal

TL;DR

This paper introduces a memristor-based selective convolutional circuit that effectively removes salt-and-pepper noise from images, outperforming traditional models at high noise levels and reducing power consumption.

Contribution

The paper presents a novel memristor-based convolutional circuit for noise removal, demonstrating improved efficiency and performance over existing models in image restoration tasks.

Findings

MSC effectively restores images with up to 50% salt-and-pepper noise

MSCE reduces power consumption by 57.6% and improves performance

MSC surpasses the theoretical benchmark of TSC at high noise densities

Abstract

In this article, we propose a memristor-based selective convolutional (MSC) circuit for salt-and-pepper (SAP) noise removal. We implement its algorithm using memristors in analog circuits. In experiments, we build the MSC model and benchmark it against a ternary selective convolutional (TSC) model. Results show that the MSC model effectively restores images corrupted by SAP noise, achieving similar performance to the TSC model in both quantitative measures and visual quality at noise densities of up to 50%. Note that at high noise densities, the performance of the MSC model even surpasses the theoretical benchmark of its corresponding TSC model. In addition, we propose an enhanced MSC (MSCE) model based on MSC, which reduces power consumption by 57.6% compared with the MSC model while improving performance.