Laser illumination compressed sensing imaging based on deep learning

Yan Liu; Hanlin Qin; Bingbin Li; Shuowen Yang; Ying Li; Yuxin Yang; Yang Yue

doi:10.1117/12.2548171

18 December 2019 Laser illumination compressed sensing imaging based on deep learning

Yan Liu, Hanlin Qin, Bingbin Li, Shuowen Yang, Ying Li, Yuxin Yang, Yang Yue

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 113420S (2019) https://doi.org/10.1117/12.2548171
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

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.

Citation Download Citation

Yan Liu, Hanlin Qin, Bingbin Li, Shuowen Yang, Ying Li, Yuxin Yang, and Yang Yue "Laser illumination compressed sensing imaging based on deep learning", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420S (18 December 2019); https://doi.org/10.1117/12.2548171

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available