Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910732619087872 |
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| author | Ding, Binghui Chen, Ling Li, Chuandong Huang, Tingwen Mitra, Sushmita |
| author_facet | Ding, Binghui Chen, Ling Li, Chuandong Huang, Tingwen Mitra, Sushmita |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_05290 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal Ding, Binghui Chen, Ling Li, Chuandong Huang, Tingwen Mitra, Sushmita Hardware Architecture Systems and Control Image and Video Processing 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. |
| title | Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal |
| topic | Hardware Architecture Systems and Control Image and Video Processing |
| url | https://arxiv.org/abs/2412.05290 |