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Denoising via MCMC-Based Lossy Compression

Jalali, Shirin and Weissman, Tsachy (2012) Denoising via MCMC-Based Lossy Compression. IEEE Transactions on Signal Processing, 60 (6). pp. 3092-3100. ISSN 1053-587X. doi:10.1109/TSP.2012.2190597.

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It has been established in the literature, in various theoretical and asymptotic senses, that universal lossy compression followed by some simple postprocessing results in universal denoising, for the setting of a stationary ergodic source corrupted by additive white noise. However, this interesting theoretical result has not yet been tested in practice in denoising simulated or real data. In this paper, we employ a recently developed MCMC-based universal lossy compressor to build a universal compression-based denoising algorithm. We show that applying this iterative lossy compression algorithm with appropriately chosen distortion measure and distortion level, followed by a simple derandomization operation, results in a family of denoisers that compares favorably (both theoretically and in practice) with other MCMC-based schemes, and with the discrete universal denoiser DUDE.

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Additional Information:© 2012 IEEE. Manuscript received July 21, 2011; revised December 17, 2011; accepted February 23, 2012. Date of publication March 12, 2012; date of current version May 11, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Roberto Lopez-Valcarce.
Subject Keywords:Compression-based denoising; denoising; Markov chain Monte Carlo; simulated annealing; universal lossy compression
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INSPEC Accession Number12725098
Issue or Number:6
Record Number:CaltechAUTHORS:20120620-090347298
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Official Citation:Jalali, S.; Weissman, T.; , "Denoising via MCMC-Based Lossy Compression," Signal Processing, IEEE Transactions on , vol.60, no.6, pp.3092-3100, June 2012 doi: 10.1109/TSP.2012.2190597 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:31971
Deposited By: Tony Diaz
Deposited On:20 Jun 2012 18:13
Last Modified:09 Nov 2021 20:02

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