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Computational ghost imaging with deep compressed sensing
Institution: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.
Keywords:computational ghost imaging  compressed sensing  deep convolution generative adversarial network  
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