Video compressive sensing via structured Laplacian modelling
Seeking a fair domain in which the signal can exhibit high sparsity is of essential significance in compressive sensing (CS). Most methods in the literature, however, use a fixed transform domain or prior information, which cannot adapt to various video contents. In this paper, we propose a video CS recovery algorithm based on the structured Laplacian model, which can effectually deal with the non-stationarity of natural videos. To build the model, structured patch groups are constructed according to the nonlocal similarity in a temporal scope. By incorporating the model into the CS paradigm, we can formulate an ℓ1-norm optimization problem, for which a solution based on the iterative shrinkage/thresholding algorithms (ISTA) is designed. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in both objective and subjective recovery quality.