A denoising algorithm seeks to remove noise, errors, or perturbations from a signal.
Extensive research has been devoted to this arena over the last several decades, and as a …

Compressed sensing is a new framework for acquiring sparse signals based on the
revelation that a small number of linear projections (measurements) of the signal contain …

Compressed sensing refers to a growing body of techniques that'undersample'high-
dimensional signals and yet recover them accurately. Such techniques make fewer …

Compressive Sampling Page 1 Compressive Sampling [from the GUEST EDITORS] Richard G.
Baraniuk, Emmanuel Candès, Robert Nowak, and Martin Vetterli IEEE SIGNAL PROCESSING …

In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

Compressed sensing posits that, within limits, one can undersample a sparse signal and yet
reconstruct it accurately. Knowing the precise limits to such undersampling is important both …

Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some
basis) using only a small number of random measurements. Existing results in compressed …

Conventional approaches to sampling signals or images follow Shannon's theorem: the
sampling rate must be at least twice the maximum frequency present in the signal (Nyquist …

We address the problem of compressed sensing (CS) with prior information: reconstruct a
target CS signal with the aid of a similar signal that is known beforehand, our prior …

This paper describes performance bounds for compressed sensing (CS) where the
underlying sparse or compressible (sparsely approximable) signal is a vector of …