Object classification through unknown random diffusers using a single-pixel diffractive network and spectrum encoding

Yi Luo; Bijie Bai; Yuhang Li; Xurong Li; Ege Çetintas; Mona Jarrahi; Aydogan Ozcan

doi:10.1117/12.2650792

17 March 2023 Object classification through unknown random diffusers using a single-pixel diffractive network and spectrum encoding

Yi Luo, Bijie Bai, Yuhang Li, Xurong Li, Ege Çetintas, Mona Jarrahi, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC12438, AI and Optical Data Sciences IV; PC124380C (2023) https://doi.org/10.1117/12.2650792
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

We report an all-optical object classification framework using a single-pixel diffractive network and spectrum encoding, classifying unknown objects through unknown random phase diffusers at the speed of light. Using this single-pixel diffractive network design, we numerically achieved a blind testing accuracy of 88.53%, classifying unknown handwritten digits through 80 unknown random diffusers that were never used during training. This framework presents a time- and energy-efficient all-optical solution for directly sensing through unknown random diffusers using a single pixel and will be of broad interest to various fields, such as security, biosensing and autonomous driving.

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Citation Download Citation

Yi Luo, Bijie Bai, Yuhang Li, Xurong Li, Ege Çetintas, Mona Jarrahi, and Aydogan Ozcan "Object classification through unknown random diffusers using a single-pixel diffractive network and spectrum encoding", Proc. SPIE PC12438, AI and Optical Data Sciences IV, PC124380C (17 March 2023); https://doi.org/10.1117/12.2650792

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