Reconstruction Of Compressed Video Via Non-Convex Minimization

This paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DT(P)V regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proximity operators for the lp-norm function, and numerous simulation results confirmed that the 3DT(P)V regularization can gain higher video reconstruction quality than the existing convex regularization and is more competitive than the existing video reconstruction algorithms.