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Cite Details

Will Landecker, Rick Chartrand and Simon DeDeo, "Robust Sparse Coding and Compressed Sensing with the Difference Map", in European Conference on Computer Vision, vol. 8691, pp. 315--329, 2014

Abstract

In compressed sensing, we wish to reconstruct a sparse signal x from observed data y. In sparse coding, on the other hand, we wish to find a representation of an observed signal y as a sparse linear combination, with coefficients x, of elements from an overcomplete dictionary. While many algorithms are competitive at both problems when x is very sparse, it can be challenging to recover x when it is less sparse. We present the Difference Map, which excels at sparse recovery when sparseness is lower. The Difference Map out-performs the state of the art with reconstruction from random measurements and natural image reconstruction via sparse coding.

BibTeX Entry

@inproceedings{landecker-2014-robust,
author = {Will Landecker and Rick Chartrand and Simon DeDeo},
title = {Robust Sparse Coding and Compressed Sensing with the Difference Map},
year = {2014},
urlpdf = {http://math.lanl.gov/Research/Publications/Docs/landecker-2014-robust.pdf},
booktitle = {European Conference on Computer Vision},
volume = {8691},
pages = {315--329}
}