Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory
Name
1687-6180-2006-026318.pdf
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1018.4 KB
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Adobe PDF
Checksum (MD5)
9c5b50c86cc1f45c6d4a2d777829fa27
Author(s)
Goyal, Vivek K.
Fletcher, Alyson K.
Rangan, Sundeep
Ramchandran, Kannan
Date Issued
March 2006
Journal
EURASIP Journal on Applied Signal Processing
Publisher
Hindawi Publishing Corporation
Citation
Fletcher, Alyson K. et al. “Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory.” EURASIP Journal on Advances in Signal Processing 2006 (2006): 1-20. Web. 30 Nov. 2011. © 2006 Alyson K. Fletcher et al.
Version
Author's final manuscript
Abstract
If a signal is known to have a sparse representation with respect to a frame, it can be estimated from a noise-corrupted observation by finding the best sparse approximation to . Removing noise in this manner depends on the frame efficiently representing the signal while it inefficiently represents the noise. The mean-squared error (MSE) of this denoising scheme and the probability that the estimate has the same sparsity pattern as the original signal are analyzed. First an MSE bound that depends on a new bound on approximating a Gaussian signal as a linear combination of elements of an overcomplete dictionary is given. Further analyses are for dictionaries generated randomly according to a spherically-symmetric distribution and signals expressible with single dictionary elements. Easily-computed approximations for the probability of selecting the correct dictionary element and the MSE are given. Asymptotic expressions reveal a critical input signal-to-noise ratio for signal recovery.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1155/ASP/2006/26318
