Robust diffractive neural networks for resilient all-optical intelligent imaging detection

Jiashuo Shi; Xinyu Zhang

doi:10.1117/12.2691204

7 March 2024 Robust diffractive neural networks for resilient all-optical intelligent imaging detection

Jiashuo Shi, Xinyu Zhang

Proceedings Volume 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 130880G (2024) https://doi.org/10.1117/12.2691204
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

Conventional imaging detection relies on electrical modulation, but it grapples with limitations in speed and adaptability within complex environments. Intelligent imaging detection, capable of manipulating micro-nano-lightfields generated by imaging objective lenses, is essential. The all-optical deep diffractive neural network (D²NN) offers a potential solution, enabling swift spatial information transformation and recognition. However, existing D²NN versions are vulnerable to optical disturbances due to their reliance on computer-based backpropagation. This paper introduces a robust diffractive neural network (SRNN) mathematical model designed to mitigate the impact of structural errors and lightwave frequency shifting without necessitating hardware modifications. The SRNN model incorporates Gaussian noise at various levels during training to emulate the error distribution of the phase mask, augmenting the network's resilience to hardware errors. Weight-Noise-Injection training assists the network in locating an error-insensitive region, thereby enhancing its robustness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiashuo Shi and Xinyu Zhang "Robust diffractive neural networks for resilient all-optical intelligent imaging detection", Proc. SPIE 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 130880G (7 March 2024); https://doi.org/10.1117/12.2691204

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KEYWORDS

Artificial neural networks

Object detection

Computer hardware

Education and training

Environmental sensing

Mathematical modeling

Phase distribution

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