A hybrid optronic neural network for image classification through scattering media

Zicheng Huang; Ziyu Gu; Mengyang Shi; Yesheng Gao

doi:10.1117/12.3006113

18 December 2023 A hybrid optronic neural network for image classification through scattering media

Zicheng Huang, Ziyu Gu, Mengyang Shi, Yesheng Gao

Proceedings Volume 12967, AOPC 2023: Computing Imaging Technology; 1296707 (2023) https://doi.org/10.1117/12.3006113
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

Image classification behind complex inhomogeneous media is a pervasive problem in computational optics. In recent years, optical neural networks have shown high accuracy and little computation costs in image classification. However, the improvements in scalability and complexity are still challenging. This paper presents an optronic speckle transformer (OPST) for image classification through scattering media. We utilize the optical self-attention mechanism to extract the speckle pattern’s local and global properties. We realize excellent speckle classification results with minimal computation costs based on OPST. The OPST improves the classification by more than 8% and reduces the network’s parameter by more than 30%, compared with optronic convolutional neural networks (OPCNN). Moreover, our OPST demonstrates high scalability with existing optical neural networks and is adaptive to more complex tasks. Our work paves the way to an all-optical approach with less computational costs for object classification through opaque media.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zicheng Huang, Ziyu Gu, Mengyang Shi, and Yesheng Gao "A hybrid optronic neural network for image classification through scattering media", Proc. SPIE 12967, AOPC 2023: Computing Imaging Technology, 1296707 (18 December 2023); https://doi.org/10.1117/12.3006113

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