Computational ghost imaging with deep compressed sensing*

doi:10.1088/1674-1056/ac0042

Computational ghost imaging with deep compressed sensing*

2021-12-01

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DOI：10.1088/1674-1056/ac0042

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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.

Computational ghost imaging with deep compressed sensing*

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