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- W2073354919 abstract "Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS). In this paper, we consider a family of compression algorithms Cr, parametrized by rate r, for a compact class of signals Q⊂Rn. The set of natural images and JPEG at different rates are examples of Q and Cr, respectively. We establish a connection between the rate–distortion performance of Cr, and the number of linear measurements required for successful recovery in CS. We then propose compressible signal pursuit (CSP) algorithm and prove that, with high probability, it accurately and robustly recovers signals from an underdetermined set of linear measurements. We also explore the performance of CSP in the recovery of infinite dimensional signals." @default.
- W2073354919 created "2016-06-24" @default.
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- W2073354919 date "2016-03-01" @default.
- W2073354919 modified "2023-10-15" @default.
- W2073354919 title "From compression to compressed sensing" @default.
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- W2073354919 doi "https://doi.org/10.1016/j.acha.2015.03.003" @default.
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