Detail reconstruction in ghost imaging with undersampling. (8th June 2021) - Jiang, Teng; Tan, Wei; Huang, Xianwei; Nan, Suqin; Bai, Yanfeng; Fu, Xiquan
Abstract: The need for edge detail reconstruction under low sampling rates is increasing for applications such as microscopic imaging, tomography and computer vision. Compressive sensing ghost imaging (CSGI) with undersampling greatly depends on the sparsity of the target imaged and edge reconstruction is not satisfactory. In this paper, we propose a ghost imaging (GI) reconstruction method based on total variation regularization GI (TVRGI) which takes advantage of the spatial sparsity of the target and the good edge-preserving property of total variation regularization to obtain a reconstructed image with a clearer edge. By imaging reconstructions for both binary and grayscale objects, our method presents a more satisfactory visual imaging effect performance than CSGI. It is also shown that TVRGI is more applicable for GI with grayscale objects.