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Candès, E.J. and Tao, T. (2005) Decoding by Linear Programming. IEEE Transactions on Information Theory, 51, 4203-4215.
http://dx.doi.org/10.1109/TIT.2005.858979

has been cited by the following article:

  • TITLE: Sparse Representation by Frames with Signal Analysis

    AUTHORS: Christopher Baker

    KEYWORDS: Compressed Sensing, Total Variation Minimization, l1-Analysis, D-Restricted Isometry Property, Tight Frames

    JOURNAL NAME: Journal of Signal and Information Processing, Vol.7 No.1, February 29, 2016

    ABSTRACT: The use of frames is analyzed in Compressed Sensing (CS) through proofs and experiments. First, a new generalized Dictionary-Restricted Isometry Property (D-RIP) sparsity bound constant for CS is established. Second, experiments with a tight frame to analyze sparsity and reconstruction quality using several signal and image types are shown. The constant is used in fulfilling the definition of D-RIP. It is proved that k-sparse signals can be reconstructed if by using a concise and transparent argument1. The approach could be extended to obtain other D-RIP bounds (i.e. ). Experiments contrast results of a Gabor tight frame with Total Variation minimization. In cases of practical interest, the use of a Gabor dictionary performs well when achieving a highly sparse representation and poorly when this sparsity is not achieved.