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

In this article, the memristor-based selective convolutional (MSC) circuit for salt-and-pepper (SAP) noise removal was proposed. In experiments, the MSC model was built and benchmarked 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%. In addition, this study proposes an enhanced MSC (MSCE) model based on MSC, which reduces power consumption by 57.6% compared with the MSC model while improving performance. The MSCE model maintains reliability when memristors experience conductance drift rates of less than 30% and yields greater than 89%.