Computational ghost imaging with deep compressed sensing
Hao Zhang(张浩)1, Yunjie Xia(夏云杰)1,2,†, and Deyang Duan(段德洋)1,2,‡
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
Abstract 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.
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11704221, 11574178, and 61675115) and the Taishan Scholar Project of Shandong Province, China (Grant No. tsqn201812059).
Hao Zhang(张浩), Yunjie Xia(夏云杰), and Deyang Duan(段德洋) Computational ghost imaging with deep compressed sensing 2021 Chin. Phys. B 30 124209
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