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

Minjoo Kim; Yelim Kim; Won Il Park

doi:10.1038/s41377-025-01904-z

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

Original Articles

- Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks
- Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks
- LSA 2025年14卷第9期页码：2628-2641
- Affiliations：
  
  Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
- Author bio：
  
  Won Il Park (wipark@hanyang.ac.kr)
- Funds：
- DOI：10.1038/s41377-025-01904-z
  中图分类号：
- 收稿日期：2024-12-31，
  
  修回日期：2025-05-16，
  
  录用日期：2025-05-21，
  
  网络出版日期：2025-07-22，
  
  纸质出版日期：2025-09-30
- Accepted：
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Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks[J]. LSA, 2025,14(9):2628-2641.

Kim, M., Kim, Y. & Park, W. I. Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks. Light: Science & Applications, 14, 2628-2641 (2025).
Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks[J]. LSA, 2025,14(9):2628-2641. DOI： 10.1038/s41377-025-01904-z.

Kim, M., Kim, Y. & Park, W. I. Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks. Light: Science & Applications, 14, 2628-2641 (2025). DOI： 10.1038/s41377-025-01904-z.

摘要

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.

关键词

Keywords

references

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