On denoising and best signal representation Export

@article{citeulike:503585, abstract = {We propose a best basis algorithm for signal enhancement in white Gaussian noise. The best basis search is performed in families of orthonormal bases constructed with wavelet packets or local cosine bases. We base our search for the \“best\” basis on a criterion of minimal reconstruction error of the underlying signal. This approach is intuitively appealing, because the enhanced or estimated signal has an associated measure of performance, namely, the resulting mean-square error. Previous approaches in this framework have focused on obtaining the most \“compact\” signal representations, which consequently contribute to effective denoising. These approaches, however, do not possess the inherent measure of performance which our algorithm provides. We first propose an estimator of the mean-square error, based on a heuristic argument and subsequently compare the reconstruction performance based upon it to that based on the Stein (1981) unbiased risk estimator. We compare the two proposed estimators by providing both qualitative and quantitative analyses of the bias term. Having two estimators of the mean-square error, we incorporate these cost functions into the search for the \“best\” basis, and subsequently provide a substantiating example to demonstrate their performance}, author = {Krim, H. and Tucker, D. and Mallat, S. and Donoho, D.}, citeulike-article-id = {503585}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=796365}, journal = {Information Theory, IEEE Transactions on}, keywords = {denoising}, number = {7}, pages = {2225--2238}, posted-at = {2007-03-22 20:23:54}, priority = {2}, title = {On denoising and best signal representation}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=796365}, volume = {45}, year = {1999} }

TY - JOUR ID - citeulike:503585 L3 - citeulike-article-id:503585 N2 - We propose a best basis algorithm for signal enhancement in white Gaussian noise. The best basis search is performed in families of orthonormal bases constructed with wavelet packets or local cosine bases. We base our search for the “best” basis on a criterion of minimal reconstruction error of the underlying signal. This approach is intuitively appealing, because the enhanced or estimated signal has an associated measure of performance, namely, the resulting mean-square error. Previous approaches in this framework have focused on obtaining the most “compact” signal representations, which consequently contribute to effective denoising. These approaches, however, do not possess the inherent measure of performance which our algorithm provides. We first propose an estimator of the mean-square error, based on a heuristic argument and subsequently compare the reconstruction performance based upon it to that based on the Stein (1981) unbiased risk estimator. We compare the two proposed estimators by providing both qualitative and quantitative analyses of the bias term. Having two estimators of the mean-square error, we incorporate these cost functions into the search for the “best” basis, and subsequently provide a substantiating example to demonstrate their performance IS - 7 JF - Information Theory, IEEE Transactions on EP - 2238 TI - On denoising and best signal representation VL - 45 SP - 2225 KW - denoising AU - Krim, H AU - Tucker, D AU - Mallat, S AU - Donoho, D PY - 1999/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=796365 ER -