A remark about orthogonal matching pursuit algorithm

A Remark on a Remark by Macaulay or Enhancing Lazard Structural Theorem

Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure

This paper suggests an upside-down tree-based orthogonal matching pursuit UDT-OMP compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree str...

A fast orthogonal matching pursuit algorithm

The problem of optimal approximation of members of a vector space by a linear combination of members of a large overcomplete library of vectors is of importance in many areas including image and video coding, image analysis, control theory, and statistics. Finding the optimal solution in the general case is mathematically intractable. Matching pursuit, and its orthogonal version, provide greedy...

The Stability of Regularized Orthogonal Matching Pursuit Algorithm

This paper studies a fundamental problem that arises in sparse representation and compressed sensing community: can greedy algorithms give us a stable recovery from incomplete and contaminated observations ? Using the Regularized Orthogonal Matching Pursuit (ROMP) algorithm, a modified version of Orthogonal Matching Pursuit (OMP) [1], which was recently introduced by D.Needell and R.Vershynin [...

Multichannel Image Estimation via Simultaneous Orthogonal Matching Pursuit

In modern imaging systems, it is possible to collect information about an image on multiple channels. The simplest example is that of a color image which consists of three channels (i.e. red, green, and blue). However, there are more complicated situations such as those that arise in hyperspectral imaging. Furthermore, most of these images are sparse or highly compressible. We need not measure ...