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- W1963850048 abstract "In this paper, we propose a novel algorithm for image reconstruction from compressive measurements of wavelet coefficients. By incorporating independent Laplace priors on separate wavelet sub-bands, the inhomogeneity of wavelet coefficient distributions and therefore the structural sparsity within images are modeled effectively. We model the problem by adopting a Bayesian formulation, and develop a fast greedy reconstruction algorithm. Experimental results demonstrate that the reconstruction performance of the proposed algorithm is competitive with state-of-the-art methods while outperforming them in terms of running times." @default.
- W1963850048 created "2016-06-24" @default.
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- W1963850048 date "2009-08-01" @default.
- W1963850048 modified "2023-09-28" @default.
- W1963850048 title "Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors" @default.
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- W1963850048 doi "https://doi.org/10.1109/ssp.2009.5278598" @default.
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