IEEE Trans Image Process - Compressive sensing via nonlocal low-rank regularization.

Tópicos

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Resumo

Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex log det ( X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery.

Resumo Limpo

sparsiti wide exploit exact reconstruct signal small number random measur recent advanc suggest structur group sparsiti often lead power signal reconstruct techniqu various compress sens cs studi paper propos nonloc lowrank regular nlr approach toward exploit structur sparsiti explor applic cs photograph mri imag also propos use nonconvex log det x smooth surrog function rank instead convex nuclear norm justifi benefit strategi use extens experi improv comput effici propos algorithm develop fast implement use altern direct multipli method techniqu experiment result shown propos nlrcs algorithm can signific outperform exist stateoftheart cs techniqu imag recoveri

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