Domain decomposition methods for compressed sensing
Numerical Analysis
2009-02-03 v1
Abstract
We present several domain decomposition algorithms for sequential and parallel minimization of functionals formed by a discrepancy term with respect to data and total variation constraints. The convergence properties of the algorithms are analyzed. We provide several numerical experiments, showing the successful application of the algorithms for the restoration 1D and 2D signals in interpolation/inpainting problems respectively, and in a compressed sensing problem, for recovering piecewise constant medical-type images from partial Fourier ensembles.
Cite
@article{arxiv.0902.0124,
title = {Domain decomposition methods for compressed sensing},
author = {Massimo Fornasier and Andreas Langer and Carola-Bibiane Schönlieb},
journal= {arXiv preprint arXiv:0902.0124},
year = {2009}
}
Comments
4 pages