Abstract

In this Letter we present a physics-enhanced deep learning approach for speckle correlation imaging (SCI), i.e., DeepSCI. DeepSCI incorporates the theoretical model of SCI into both the training and test stages of a neural network to achieve interpretable data preprocessing and model-driven fine-tuning, allowing the full use of data and physics priors. It can accurately reconstruct the image from the speckle pattern and is highly scalable to both medium perturbations and domain shifts. Our experimental results demonstrate the suitability and effectiveness of DeepSCI for solving the problem of limited generalization generally encountered in data-driven approaches.

© 2023 Optica Publishing Group

Imaging through a scattering medium via model-driven deep learning

Fu Liu, Xiangfeng Meng, Yongkai Yin, and Xiulun Yang
Opt. Lett. 48(20) 5285-5288 (2023)

Single-pixel imaging using physics enhanced deep learning

Fei Wang, Chenglong Wang, Chenjin Deng, Shensheng Han, and Guohai Situ
Photon. Res. 10(1) 104-110 (2022)

Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media

Yunzhe Li, Yujia Xue, and Lei Tian
Optica 5(10) 1181-1190 (2018)

Physical origin and boundary of scalable imaging through scattering media: a deep learning-based exploration

Xuyu Zhang, Shengfu Cheng, Jingjing Gao, Yu Gan, Chunyuan Song, Dawei Zhang, Songlin Zhuang, Shensheng Han, Puxiang Lai, and Honglin Liu
Photon. Res. 11(6) 1038-1046 (2023)

Deep-learning-based 3D blood flow reconstruction in transmissive laser speckle imaging

Ruoyu Chen, Shanbao Tong, and Peng Miao
Opt. Lett. 48(11) 2913-2916 (2023)