J
José Luis Paredes
Researcher at University of Los Andes
Publications - 78
Citations - 1417
José Luis Paredes is an academic researcher from University of Los Andes. The author has contributed to research in topics: Weighted median & Compressed sensing. The author has an hindex of 18, co-authored 77 publications receiving 1360 citations. Previous affiliations of José Luis Paredes include BAE Systems & Alcatel-Lucent.
Papers
More filters
Journal ArticleDOI
Ultra-Wideband Compressed Sensing: Channel Estimation
TL;DR: Extensive simulations show that for different propagation scenarios and UWB communication channels, detectors based on CS channel estimation outperform traditional correlator using just 1/3 of the sampling rate leading thus to a reduced use of analog-to-digital resources in the channel estimation stage.
Journal ArticleDOI
Zero-Order Statistics: A Mathematical Framework for the Processing and Characterization of Very Impulsive Signals
TL;DR: This paper introduces a new approach to statistical moment characterization which is well defined over all processes with algebraic or lighter tails, and derives a ZOS framework for location estimation that gives rise to a novel mode-type estimator with important optimality properties under very impulsive noise.
Journal ArticleDOI
Recursive weighted median filters admitting negative weights and their optimization
TL;DR: A recursive weighted median (RWM) filter structure admitting negative weights is introduced, and a novel "recursive decoupling" adaptive optimization algorithm for the design of this class of recursive WM filters is also introduced.
Proceedings Article
Variable density compressed image sampling
TL;DR: In this article, a variable density sampling strategy was proposed to exploit the a priori information about the statistical distributions that natural images exhibit in the wavelet domain, and the proposed sampling approach can be applied to several transform domains leading to simple implementations.
Journal ArticleDOI
Weighted median image sharpeners for the World Wide Web
TL;DR: A class of robust weighted median (WM) sharpening algorithms is developed that can prove useful in the enhancement of compressed or noisy images posted on the World Wide Web as well as in other applications where the underlying images are unavoidably acquired with noise.