Compressive Sensing Ghost Imaging Based on Neighbor Similarity

In order to improve the imaging quality of ghost imaging and solve the problem of high distortion factor under low sampling ratio, we propose a compressive sensing ghost imaging method based on neighbor similarity (NSGI). The neighbor similarity embodied in the correlation between image pixels contains abundant information regarding the spatial structure of the object. We analyze the principle of compressive sensing ghost imaging and use the neighbor similarity to evaluate undetected targets. According to the principle of greedy algorithm, we adopt the neighbor similarity to optimize the process of image reconstruction, and set up the threshold value of the correlation coefficient to reduce computation load and improve precision. The simulation and experimental results show that compared with the traditional ghost imaging, NSGI can obtain high-quality images based on a low sampling frequency, which will further facilitate the practical application of ghost imaging.