Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks

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...

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Vydané v:Light, science & applications Ročník 14; číslo 1; s. 248 - 14
Hlavní autori: Kim, Minjoo, Kim, Yelim, Park, Won Il
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 22.07.2025
Springer Nature B.V
Nature Publishing Group
Predmet:
639/624/1075/401
639/624/1107/510
Data compression
Efficiency
Energy consumption
Image processing
Lasers
Microwaves
Neural networks
Noise reduction
Optical and Electronic Materials
Optical Devices
Optics
Performance evaluation
Photonics
Physics
Physics and Astronomy
RF and Optical Engineering
Simulation
ISSN:2047-7538, 2095-5545, 2047-7538
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2047-7538
2095-5545
2047-7538
DOI:10.1038/s41377-025-01904-z