Bayesian approach to best basis selection

作者：Jean‐Christophe Pesquet, Hamid Krim, D. Leporini, Elizabeth A. Hamman · 年份：2002 · DOI：10.1109/icassp.1996.548005 · 被引用次数：56 · 研究领域：Image and Signal Denoising Methods、Blind Source Separation Techniques、Statistical and numerical algorithms

Wavelet packets and local trigonometric bases provide an efficient framework and fast algorithms to obtain a "best basis" or "best representation" of deterministic signals. Applying these deterministic techniques to stochastic processes may, however, lead to variable results. We revisit this problem and introduce a prior model on the underlying signal in noise and account for the contaminating noise model as well. We thus develop a Bayesian-based approach to the best basis problem, while preserving the classical tree search efficiency.