A comparative investigation on the use of compressive sensing methods in computational ghost imaging

摘要

Usually, a large number of patterns are needed in the computational ghost imaging (CGI). In this work, the possibilitiesto reduce the pattern number by integrating compressive sensing (CS) algorithms into the CGI process are systematicallyinvestigated. Based on the different combinations of sampling patterns and image priors for the L1-norm regularization,different CS-based CGI approaches are proposed and implemented with the iterative shrinkage thresholding algorithm.These CS-CGI approaches are evaluated with various test scenes. According to the quality of the reconstructed imagesand the robustness to measurement noise, a comparison between these approaches is drawn for different sampling ratios,noise levels, and image sizes.