A dual structured-sparsity model for compressive-sensed video reconstruction

The compressive sensing theory indicates that robust reconstruction of signals can be obtained from far fewer measurements than those required by the Nyquist theorem. Thus, it has great potential in video acquisition and processing in that it can tremendously save the complex compression required by traditional video coding standards. In this paper, we consider reconstruction of compressive-sensed videos and propose a novel structured-sparsity model with a dual prediction strategy. This structured-sparsity model goes beyond simple sparsity and characterizes the intrinsic structure within the transform coefficients. Also, it exploits the sparsity of the residual between the current patch and its prediction. The prediction process is comprised of a dual strategy, which integrates the advantages of the ambient pixel domain and the measurement domain. In addition, an effective optimization method is designed for solving the formulated problem derived from the model. Experiments demonstrate that the proposed algorithm outperforms the state-of-the art methods for compressive-sensed video reconstruction in both subjective and objective quality.