The development of wavelet theory has in recent years spawned applications in signal
processing, in fast algorithms for integral transforms, and in image and function …

Data that represent complex and multivariate processes are well known to be multiscale due
to the variety of changes that could occur in a process with different localizations in time and …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …

This book gives a systematic, comprehensive, and unified account of modern nonparametric
statistics of density estimation, nonparametric regression, filtering signals, and time series …

We introduce a nondiagonal seismic denoising method based on the continuous wavelet
transform with hybrid block thresholding (BT). Parameters for the BT step are adaptively …

Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of
spatially-variable objects. We discuss in detail wavelet methods in nonparametric …

In recent years there has been a considerable development in the use of wavelet methods in
statistics. As a result, we are now at the stage where it is reasonable to consider such …

Recently there has been significant development in the use of wavelet methods in various
data mining processes. However, there has been written no comprehensive survey …

This thoroughly updated new edition presents state of the art sparse and multiscale image
and signal processing. It covers linear multiscale geometric transforms, such as wavelet …