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 …

In emergency situations, actions that save lives and limit the impact of hazards are crucial. In
order to act, situational awareness is needed to decide what to do. Geolocalized photos and …

In spite of their remarkable success in signal processing applications, it is now widely
acknowledged that traditional wavelets are not very effective in dealing multidimensional …

Historically, the theory of Hilbert spaces originated from David Hilbert's (1862–1943) work
on quadratic forms in infinitely many variables with their applications to integral equations …

Multiresolution methods are deeply related to image processing, biological and computer
vision, and scientific computing. The curvelet transform is a multiscale directional transform …

In this paper we show that shearlets, an affine-like system of functions recently introduced by
the authors and their collaborators, are essentially optimal in representing 2-dimensional …

Wavelets and their associated transforms are highly efficient when approximating and
analyzing one-dimensional signals. However, multivariate signals such as images or videos …

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 …

Multi-focus image fusion plays an important role in the application of computer vision. In the
process of image fusion, there may be blurring and information loss, so it is our goal to …

This survey gives an introduction to redundant signal representations called frames. These
representations have recently emerged as yet another powerful tool in the signal processing …