Computational ghost imaging based on a conditional generation countermeasure network under a low sampling rate.

Abstract

In this paper, an end-to-end depth neural network based on a conditional generative adversarial network for computational ghost imaging (CGANCGI) is proposed to restore clear object images with high quality at a sub-Nyquist sampling rate. The 2D light signal collected by a CMOS camera and the gray image of the original measured object are used as the input of the network model; then, the CGANCGI network is trained, and the measured object image is recovered directly from the 2D light signal. Experiments have verified that the proposed method only needs 1/10 of traditional deep learning samples to achieve fast image restoration with high-quality, and its peak signal-to-noise ratio and structural similarity are, respectively, four to six times and five to seven times higher than those of the original image, which prove that our method has practical application prospects in ghost imaging under low sampling rates.