Reconstruction of compressed-sensed video using compound regularization

This paper introduces a novel reconstruction model with compound regularization to recover compressed-sensed video sequences. For a target frame, the compound regularization consists of total variation (TV) norm of the frame, l₁ norm of the frame in a certain transform domain, and TV norm of the residual between the frame and its prediction. The first two terms in the compound regularization are used to describe image characteristics, while the third term exploits inter-frame correlation within video sequences. To solve the minimization problem, a new splitting objective function is considered, and it is divided into sub-problems that are easy to solve. In addition, bivariate shrinkage method is integrated into the proposed algorithm so that high quality of reconstruction results can be guaranteed. Experimental results show that the proposed algorithms are substantially superior to state-of-the-art reconstruction methods.