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Proceedings Paper

Laser illumination compressed sensing imaging based on deep learning
Author(s): Yan Liu; Hanlin Qin; Bingbin Li; Shuowen Yang; Ying Li; Yuxin Yang; Yang Yue
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Paper Abstract

In order to compensate for the low spatial resolution of laser illumination imaging system due to the single photon detector with small number of pixels. In order to solve this problem, we demonstrated a laser illumination imaging system with compressed coded and introduced the application of deep learning in compressed sensing (CS) image reconstruction based on residual network. Specifically, by considering the priori information of sparsity, the better imaging results with much higher resolution could be obtained with a small amount of observation data. The digital micro-mirror device (DMD) is used to achieve sparse coding in this work. We designed to use two detectors to collect information in two reflection directions of DMD, which can reduce samples by 50%. In addition, considering that the time complexity of traditional CS reconstruction methods is too high, so we introduced CS reconstruction method based on residual network into our work, and did the simulation experiments with our data. According to the experimental results, our method performed better at the perspective of image quality evaluation index PSNR and consumption time in reconstruction process.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420S (18 December 2019); doi: 10.1117/12.2548171
Show Author Affiliations
Yan Liu, Xidian Univ. (China)
Hanlin Qin, Xidian Univ. (China)
Bingbin Li, Xidian Univ. (China)
Shuowen Yang, Xidian Univ. (China)
Ying Li, Xidian Univ. (China)
Yuxin Yang, Xidian Univ. (China)
Yang Yue, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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