High speed ghost imaging based on a heuristic algorithm and deep learning

被引：0

作者：

黄祎祎 ^{[1
,2
]}

欧阳琛 ^{[1
,2
]}

方可 ^{[1
,2
]}

董玉峰 ^{[1
]}

张杰 ^{[1
,3
]}

陈黎明 ^{[3
,4
]}

吴令安 ^{[1
,2
]}

机构：

[1] Institute of Physics, Chinese Academy of Sciences

[2] University of Chinese Academy of Sciences

[3] IFSA Collaborative Innovation Center and School of Physics and Astronomy, Shanghai Jiao Tong University

[4] College of Engineering Physics, Shenzhen Technology University

来源：

Chinese Physics B | 2021年 / 06期

关键词：

D O I：

暂无

中图分类号：

TP391.41 []; TP18 [人工智能理论];

学科分类号：

080203 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.

引用

页码：308 / 314

页数：7

共 34 条

[1] Image-free classification of fast-moving objects using "learned" structured illumination and single-pixel detection [J].