A fast and accurate first-order algorithm for compressed sensing

Authors:
J. Bobin;E. J. Candès
Affiliations:
Applied and Computational Mathematics, California Institute of Technology, Pasadena, CA;Applied and Computational Mathematics, California Institute of Technology, Pasadena, CA
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 4
Cited 1

Smooth minimization of non-smooth functions

Mathematical Programming: Series A and B
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

IEEE Transactions on Information Theory
Compressed sensing

IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

IEEE Transactions on Information Theory

NESTA: A Fast and Accurate First-Order Method for Sparse Recovery

SIAM Journal on Imaging Sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a new, fast and accurate algorithm for solving problems in the area of compressed sensing, and more generally, in the area of signal and image reconstruction from indirect measurements. This algorithm is inspired by recent progress in the development of novel first-order methods in convex optimization, most notably Nesterov's smoothing technique. In particular, there is a crucial property thatmakes thesemethods extremely efficient for solving compressed sensing problems. Numerical experiments show the promising performance of our method to solve problems which involve the recovery of signals spanning a large dynamic range.