The orthogonal matching pursuit (OMP) algorithm employs an overcomplete dictionary in an
adaptive nonlinear process for signal decomposition. An efficient tree-search-based OMP …

We present an algorithm, dubbed Multi-Branch Matching Pursuit (MBMP), to solve the
sparse recovery problem over redundant dictionaries. MBMP combines three different …

Les décompositions parcimonieuses décrivent un signal comme une combinaison d'un petit
nombre de formes de base, appelées atomes. Le dictionnaire d'atomes, crucial pour …

The traditional Gnutella model can solve the centralized problem of network structure.
However, its consumption of a great deal of bandwidth is prone to result in the network …

Greedy algorithms have leveraged sparse signal models for parameter estimation purposes
in applications including bearing estimation and direction-of-arrival (DOA) estimation. A …

We present a simple and robust optimization method for, giving an overcomplete Parseval
frame, an lp-norm value, and an image, obtaining the representation vector, up to that lp …

We present a new approach to extract predefined circular objects, such as trees and oil
tanks, from aerial images. We consider a stochastic approach based on an object process …

In the modern world, current needs have provoked a fast increase in the interest in image
processing applications. In the beginning, these applications were approximated with …