All the studies on wavelets so far, whether from multiresolution analyses or from fre-quency constructions, consider wavelets whose dilation sets consist of all the powers of a single …

The idea behind these time-frequency joint representations is to cut the signal of interest into several parts and then analyze the parts separately. It is clear that analyzing a signal this way …

Wavelets are a mathematical tool for hierarchically decomposing functions. They allowa function tobedescribed intermsofa coarse overall shape, plus details that range from broad to narrow. …

ABSTRACT This paper attempts to discuss the prominence and impact of Wavelet Trans form and its core applications based on its striking features and to state properties and other special …

Abstract Sparsity based methods, such as wavelets, have been state-of-the-art for more than 20 years for inverse problems before being overtaken by neural net-works. In particular, U-nets …

We propose a novel method for constructing wavelet transforms of functions de ned on the vertices of an arbitrary nite weighted graph. Our approach is based on de ning scaling using …

ABSTRACT The GHM multi-level discrete wavelet transform is proposed as preprocessing for image super resolution with convolutional neural networks. Previous works perform analysis …