Mutual structure ghost imaging under low sampling

Abstract

The realization of high-quality imaging under low sampling is an effective way to solve the practical application of ghost imaging. In this paper, we present an advanced framework of compressed ghost imaging under low sampling. During the imaging process, the regularization and mutual structure filtering operations are performed alternately, which we call mutual structure ghost imaging (MSGI). In the joint filtering of our proposed scheme, the mutual-structural information contained in both the reference image and the target one is applied to enhance the capability of important edge preserving. Thus, high-resolution ghost imaging results can be obtained under low sampling. Moreover, we have adopted a fast-converging iterative format to obtain better imaging results with fewer iterations. Simulation and experimental results show that the proposed method can achieve high-quality imaging results from the random speckle patterns under low sampling and promote the practical process of ghost imaging.