Video Compressive Sensing Reconstruction via Reweighted Residual Sparsity

The compressive sensing (CS) theory indicates that robust reconstruction of signals can be obtained from far fewer measurements than those required by the Nyquist-Shannon theorem. Thus, CS has great potential in video acquisition and processing, considering that it makes the subsequent complex data compression unnecessary. In this paper, we propose a novel algorithm for effectively reconstructing videos from CS measurements. The algorithm comprises double phases, of which the first phase exploits intra-frame correlation and provides good initial recovery for each frame, and the second phase iteratively enhances reconstruction quality by alternating interframe multihypothesis (MH) prediction and sparsity modeling of residuals in a weighted manner. The weights of residual coefficients are updated in each iteration using a statistical method based on the MH predictions. These procedures are performed in the unit of overlapped patches such that potential blocking artifacts can be effectively suppressed through averaging. In addition, we devise an effective scheme based on the split Bregman iteration algorithm to solve the formulated weighted l(1) minimization problem. The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in both objective and subjective reconstruction quality.