Sparse and redundant representation modeling of data assumes an ability to describe
signals as linear combinations of a few atoms from a pre-specified dictionary. As such, the …

At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

The richness of natural images makes the quest for optimal representations in image
processing and computer vision challenging. The latter observation has not prevented the …

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 …

It is now widely acknowledged that analyzing the intrinsic geometrical features of the
underlying image is essential in many applications including image processing. In order to …

We introduce a digital waveletlike transform, which is tailored specifically for representing
seismic data. The transform provides a multiscale orthogonal basis with basis functions …

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 …

We propose a direction-adaptive DWT (DA-DWT) that locally adapts the filtering directions to
image content based on directional lifting. With the adaptive transform, energy compaction is …

In this work a new set of edge-adaptive transforms (EATs) is presented as an alternative to
the standard DCTs used in image and video coding applications. These transforms avoid …

In order to get an efficient image representation we introduce a new adaptive Haar wavelet
transform, called Tetrolet Transform. Tetrolets are Haar-type wavelets whose supports are …