Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal
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....
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2412.05290 |