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

• 作者: Yi-Yi Huang Chen Ou-Yang Ke Fang Yu-Feng Dong Jie Zhang Li-Ming Chen Ling-An Wu
• 来源期刊: 中国物理B（英文版）
• 年，卷（期）: 2021,（6）
• 所属期刊栏目: ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
• 页码范围: 325-332
• 页数: 8页
• 语种: 英文
• DOI: 10.1088/1674-1056/abea8c

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 over-lapped matrices are then designed to shorten and optimize encoding of the overlapped pattems,which are shown to be much superior to the random matrices.In addition,we apply deep leaming 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.