用忆阻器电路实现高密度椒盐噪声去除，效果媲美甚至超越传统方法。
Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal
- 基于忆阻器的模拟电路实现选择性卷积，直接在硬件层面处理噪声
- 在50%噪声密度下性能与三值模型相当，更高密度时反而更优
- 改进版功耗降低57.6%，适合低功耗图像修复场景
本文提出一种基于忆阻器的选择性卷积（MSC）电路，用于去除椒盐（SAP）噪声。通过在模拟电路中实现忆阻器算法，构建了MSC模型，并与三值选择性卷积（TSC）模型进行对比。实验表明，该模型在噪声密度高达50%时，能有效恢复受污染图像，在定量指标和视觉质量上与TSC模型相当；在高噪声密度下，其性能甚至超过对应TSC模型的理论基准。此外，基于MSC提出的增强型模型（MSCE），相较原模型降低57.6%功耗，同时提升性能。
原文摘要 · Abstract (English)
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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