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.
作者
Yi-Yi Huang
Chen Ou-Yang
Ke Fang
Yu-Feng Dong
Jie Zhang
Li-Ming Chen
Ling-An Wu
黄祎祎;欧阳琛;方可;董玉峰;张杰;陈黎明;吴令安(Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;IFSA Collaborative Innovation Center and School of Physics and Astronomy,Shanghai Jiao Tong University,Shanghai 200240,China;College of Engineering Physics,Shenzhen Technology University,Shenzhen 518118,China)
基金
supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0403301, 2017YFB0503301, and2018YFB0504302)
the National Natural Science Foundation of China (Grant Nos. 11991073, 61975229, and Y8JC011L51)
the Key Program of CAS (Grant No. XDB17030500)
the Civil Space Project (Grant No. D040301)
the Science Challenge Project (Grant No. TZ2018005)。
