Traditional ghost imaging usually suffers from two problems. First, it usually offers poor images even if the measurement numbers are much larger than the pixels of images to be imaged. Second, the number of samples of ghost imaging should be lower. In this paper, a Group-based sparse representation ghost imaging (GSRGI) reconstruction scheme is proposed. The proposed GSRGI uses the structure group as the basic unit of sparse representation, which is composed of patches with similar structures, fully reflects the local sparsity and non-local self-similarity of natural images. To make GSRGI computational complexity lower, we combine the method of image blocking with computational ghost imaging. Through numerical simulation and experiment, GSRGI can obtain high image quality under a low sampling rate, it is better than other schemes in both quantitative analysis and visual perception.
The application of compressed sensing(GS) theory in ghost imaging reduces the sampling required for image reconstruction, thus improving the reconstruction efficiency. Due to its sparsity constraints on objects, the algorithm performs better on sparse and smooth images. Many studies have been carried out on sparse representation of objects and the solution of constraint equations. Different from the previous method, using GS method after orthogonalize the reference patterns as a pretreatment method to reconstruct image is proposed in this paper. We compare the simulation and experimental results of the original GS algorithm and the GS algorithm with using pretreated reference patterns. The results show that the pretreatment method improves the quality of reconstructed images in simulation and experimental. It is proved that the pretreatment method is a feasible method to improve the quality of reconstructed images.
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