Imaging through unknown scattering media based on physics-informed learning

被引:103
作者
Zhu, Shuo [1 ]
Guo, Enlai [1 ]
Gu, Jie [1 ]
Bai, Lianfa [1 ]
Han, Jing [1 ]
机构
[1] Nanjing Univ Sci & Technol, Jiangsu Key Lab Spectral Imaging & Intelligent Se, Nanjing 210094, Peoples R China
基金
中国国家自然科学基金;
关键词
RECONSTRUCTION; LAYERS;
D O I
10.1364/PRJ.416551
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Imaging through scattering media is one of the hotspots in the optical field, and impressive results have been demonstrated via deep learning (DL). However, most of the DL approaches are solely data-driven methods and lack the related physics prior, which results in a limited generalization capability. In this paper, through the effective combination of the speckle-correlation theory and the DL method, we demonstrate a physics-informed learning method in scalable imaging through an unknown thin scattering media, which can achieve high reconstruction fidelity for the sparse objects by training with only one diffuser. The method can solve the inverse problem with more general applicability, which promotes that the objects with different complexity and sparsity can be reconstructed accurately through unknown scattering media, even if the diffusers have different statistical properties. This approach can also extend the field of view (FOV) of traditional speckle-correlation methods. This method gives impetus to the development of scattering imaging in practical scenes and provides an enlightening reference for using DL methods to solve optical problems. (C) 2021 Chinese Laser Press
引用
收藏
页码:B210 / B219
页数:10
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