Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks
Abstract This study introduces an optical neural network (ONN)-based autoencoder for efficient image processing, utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks. To address the challenges in efficient decoding, we propose a method that optimizes output pr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-07-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-025-01904-z |
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| Summary: | Abstract This study introduces an optical neural network (ONN)-based autoencoder for efficient image processing, utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks. To address the challenges in efficient decoding, we propose a method that optimizes output processing through scalar multiplications, enhancing performance in generating higher-dimensional outputs. By employing on-system iterative tuning, we mitigate hardware imperfections and noise, progressively improving image reconstruction accuracy to near-digital quality. Furthermore, our approach supports noise reduction and optical image generation, enabling models such as denoising autoencoders, variational autoencoders, and generative adversarial networks. Our results demonstrate that ONN-based systems have the potential to surpass the energy efficiency of traditional electronic systems, enabling real-time, low-power image processing in applications such as medical imaging, autonomous vehicles, and edge computing. |
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| ISSN: | 2047-7538 |