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From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high probability when it exhibits sparsity in some domain. Most of the conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet and gradient domain) for the entirety of a signal, which are irrespective...
In compressive sensing (CS), the seeking of a fair domain is of essentially significance to achieve a high enough degree of signal sparsity. Most methods in the literature, however, use a fixed transform domain or prior information that cannot exhibit enough sparsity for various images. Superiorly, we propose an algorithm to explore the structured Laplacian sparsity of DCT coefficients, which can...
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