Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization

This letter proposes a novel compressive sensing reconstruction method for correlated images by using joint regularization, where a compensation-based adaptive total variation (CATV) regularization and a multi-image nonlocal low-rank (MNLR) regularization are included. In CATV, local weights are assigned to the residual values in the gradient domain so as to constrain the regularization strength at each pixel. In MNLR, the search of similar patches goes across different images so that both self-similarity and inter-image similarity are explored. Afterward, an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. The effectiveness of the proposed approach is demonstrated with experiments on both multiview images and video sequences.