Sparse filter correlation model based joint reconstruction in distributed compressive video sensing

Distributed compressive sensing (DCS) is a new technique that provides a low-complexity sub-Nyquist signal acquisition and reconstruction via a small number of random linear projections. In this paper, we propose sparse filter correlation model (SFCM) to exploit the correlations among successive video frames under the framework of distributed compressive video sensing (DCVS). At the central decoder, joint reconstruction is accomplished with the assistance of modified belief propagation (BP) algorithm, which is an efficient method for solving Bayesian inference problem. Simulation results illustrate that the proposed method provides better PSNR performance than joint sparse model 1 (JSM1) for DCVS.