Denoising by sparse approximation: error bounds based on rate-distortion theory

  • Authors:

  • Alyson K. Fletcher;Sundeep Rangan;Vivek K. Goyal;Kannan Ramchandran

  • Affiliations:

  • Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA;Flarion Technologies Inc., Bedminster, NJ;Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA;Department of Electrical Engineering and Computer Sciences, College of Engineering, University of California, Berkeley, CA

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2006

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Abstract

If a signal x is known to have a sparse representation with respect to a frame, it can be estimated from a noise-corrupted observation y by finding the best sparse approximation to y. 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.