Non-Line-of-Sight Imaging Through Deep Learning

被引：0
|
作者：
Yu T. [1 ,2 ]
Qiao M. [1 ,2 ]
Liu H. [1 ]
Han S. [1 ]
机构：
[1] Key Laboratory for Quantum Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai
[2] Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing
来源：
Guangxue Xuebao/Acta Optica Sinica | 2019年 / 39卷 / 07期
关键词：
Deep learning; Imaging systems; Non-line-of-sight imaging; Residual model; Semantic segmentation;
D O I：
10.3788/AOS201939.0711002
中图分类号：
学科分类号：
摘要：
Aiming at the problem of non-line-of-sight imaging under incoherent illumination, we propose a solution based on deep learning. Combining the classical semantic segmentation and residual model in the field of computer vision, a URNet network structure is constructed and the classical bottleneck layer structure is improved. The experimental results show that the improved model has more details of recovery images and generalization ability. Compared with speckle autocorrelation imaging method under incoherent illumination, the recovery performance of this method is greatly improved. © 2019, Chinese Lasers Press. All right reserved.
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