tailieunhanh - Báo cáo hóa học: " Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 26318 Pages 1-19 DOI ASP 2006 26318 Denoising by Sparse Approximation Error Bounds Based on Rate-Distortion Theory Alyson K. Fletcher 1 Sundeep Rangan 2 Vivek K Goyal 3 and Kannan Ramchandran4 1 Department of Electrical Engineering and Computer Sciences University of California Berkeley CA 94720-1770 USA 2Flarion Technologies Inc. Bedminster NJ 07921 USA 3 Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA 02139-4307 USA 4 Department of Electrical Engineering and Computer Sciences College of Engineering University of California Berkeley CA 94720-1770 USA Received 9 September 2004 Revised 6 June 2005 Accepted 30 June 2005 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. Copyright 2006 Alyson K. Fletcher et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use .

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