Image classification through scattering media using optronic convolutional neural networks

Zicheng Huang; Ziyu Gu; Yesheng Gao

doi:10.1117/12.2662935

12 April 2023 Image classification through scattering media using optronic convolutional neural networks

Zicheng Huang, Ziyu Gu, Yesheng Gao

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 125652Y (2023) https://doi.org/10.1117/12.2662935
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

Abstract

Object classification behind a complex inhomogenous medium remains a significant challenge in many fields. Valid information is hardly extracted from speckles owing to the distortion of scattering media. Recent years deep learning has shown powerful capability in classifying object from scattered speckle patterns. However, largescale computations and pure digital procedures set a challenging task for deep neural networks running in optics. Here, we present an optronic technique for object classification through random diffuser media. A group of Fourier lens and a programmable spatial light modulator, form an optronic convolutional neural networks(OPCNN) with optimized kernels. The CMOS camera not only works as an image detection sensor but a non-linear activation layer by the curve built-in. We demonstrated the technique classification result using the airy disk intensity of every candidate channel. The trained OPCNN shows high-quality object predictions on the speckle patterns. Our work paves the way to an all-optical approach for imaging through scattering media.

Citation Download Citation

Zicheng Huang, Ziyu Gu, and Yesheng Gao "Image classification through scattering media using optronic convolutional neural networks", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 125652Y (12 April 2023); https://doi.org/10.1117/12.2662935

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