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Conventional compressive sampling methods cannot efficiently exploit structured sparsity for sampling multidimensional signals like video sequences. In this paper, we propose a fully decomposable compressive sampling model that adopts the Kronecker product framework to exploit the structured sparsity spanning multidimensional signals. It enables efficient sampling in a progressive fashion by retaining...
Existing sparse representation with subspace learning is hampered by the intersection of subspaces of bases. With structured sparsity to enable the prior knowledge of signal statistics, this paper proposes a novel compressive video sampling by subspace learning to minimize the intersection of subspaces. As the measurement, the block coherence is optimized with the regularized learning to generate...
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