Sequential Bayesian wavelet denoising

Abstract

We propose a wavelet model that incorporates coefficient correlation and is expressed in state-space form, allowing the development and application of sequential estimation algorithms for wavelet denoising. We detail a sequential simulation-based estimation algorithm based on particle filters. This algorithm allows Bayesian wavelet denoising to be performed on-line, enabling it to process a vast dataset, and it is intrinsically parallelizable. The experiments indicate that the algorithm performance is comparable to the majority of Bayesian framework batch-based algorithms.