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Learning-based phase imaging using a low-bit-depth pattern

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Abstract

Phase imaging always deals with the problem of phase invisibility when capturing objects with existing light sensors. However, there is a demand for multiplane full intensity measurements and iterative propagation process or reliance on reference in most conventional approaches. In this paper, we present an end-to-end compressible phase imaging method based on deep neural networks, which can implement phase estimation using only binary measurements. A thin diffuser as a preprocessor is placed in front of the image sensor to implicitly encode the incoming wavefront information into the distortion and local variation of the generated speckles. Through the trained network, the phase profile of the object can be extracted from the discrete grains distributed in the low-bit-depth pattern. Our experiments demonstrate the faithful reconstruction with reasonable quality utilizing a single binary pattern and verify the high redundancy of the information in the intensity measurement for phase recovery. In addition to the advantages of efficiency and simplicity compared to now available imaging methods, our model provides significant compressibility for imaging data and can therefore facilitate the low-cost detection and efficient data transmission.

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DOI:10.1364/PRJ.398583

所属栏目:Image Processing and Image Analysis

基金项目:National Key Research and Development Program of China10.13039/501100012166; Shanghai Aerospace Technology Renovation Fund;

收稿日期:2020-05-25

录用日期:2020-08-13

网络出版日期:2020-08-14

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Zhenyu Zhou:Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Jun Xia:Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Jun Wu:Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Chenliang Chang:Department of Bioengineering, University of California, Los Angeles, California 90095, USA
Xi Ye:Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China
Shuguang Li:Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China
Bintao Du:Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Hao Zhang:Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Guodong Tong:Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China

联系人作者:Jun Xia(xiajun@seu.edu.cn)

备注:National Key Research and Development Program of China10.13039/501100012166; Shanghai Aerospace Technology Renovation Fund;

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引用该论文

Zhenyu Zhou, Jun Xia, Jun Wu, Chenliang Chang, Xi Ye, Shuguang Li, Bintao Du, Hao Zhang, and Guodong Tong, "Learning-based phase imaging using a low-bit-depth pattern," Photonics Research 8(10), 1624-1633 (2020)

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