Computational ghost imaging with deep compressed sensing  

作　　者：Hao Zhang Yunjie Xia Deyang Duan 张浩;夏云杰;段德洋(School of Physics and Physical Engineering,Qufu Normal University,Qufu 273165,China;Shandong Provincial Key Laboratory of Laser Polarization and Information Technology,Research Institute of Laser,Qufu Normal University,Qufu 273165,China)

机构地区：[1]School of Physics and Physical Engineering,Qufu Normal University,Qufu 273165,China [2]Shandong Provincial Key Laboratory of Laser Polarization and Information Technology,Research Institute of Laser,Qufu Normal University,Qufu 273165,China

出　　处：《Chinese Physics B》2021年第12期455-458,共4页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Grant Nos.11704221,11574178,and 61675115);the Taishan Scholar Project of Shandong Province,China(Grant No.tsqn201812059)。

摘　　要：Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.

关 键 词：computational ghost imaging compressed sensing deep convolution generative adversarial network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O439[自动化与计算机技术—计算机科学与技术]