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Authors: Igor Ciril 1 ; Jérôme Darbon 2 and Yohann Tendero 3

Affiliations: 1 Institut Polytechnique des Sciences Avancées, France ; 2 Brown University, United States ; 3 Université Paris-Saclay, France

ISBN: 978-989-758-290-5

Keyword(s): Basis Pursuit, Compressive Sensing, Inverse Scale Space, Sparsity, $\ell^1$ Regularized Linear Problems, Non-smooth Optimization, Maximal Monotone Operator, Phase Transition.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Coding and Compression ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Image Generation Pipeline: Algorithms and Techniques

Abstract: This paper considers l1-regularized linear inverse problems that frequently arise in applications. One striking example is the so called compressive sensing method that proposes to reconstruct a high dimensional signal u P Rn from low dimensional measurements Rm Q b  Au, m ! n. The basis pursuit is another example. For most of these problems the number of unknowns is very large. The recovered signal is obtained as the solution to an optimization problem and the quality of the recovered signal directly depends on the quality of the solver. Theoretical works predict a sharp transition phase for the exact recovery of sparse signals. However, to the best of our knowledge, other state-of-the-art algorithms are not effective enough to accurately observe this transition phase. This paper proposes a simple algorithm that computes an exact l1 minimizer under the constraints Au  b. This algorithm can be employed in many problems: as soon as A has full row rank. In addition, a numerical compar ison with standard algorithms available in the literature is exhibited. These comparisons illustrate that our algorithm compares advantageously: the aforementioned transition phase is empirically observed with a much better quality. (More)

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Paper citation in several formats:
Ciril I., Darbon J. and Tendero Y. (2018). A Simple and Exact Algorithm to Solve l1 Linear Problems - Application to the Compressive Sensing Method.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 54-62. DOI: 10.5220/0006624600540062

@conference{visapp18,
author={Igor Ciril and Jérôme Darbon and Yohann Tendero},
title={A Simple and Exact Algorithm to Solve l1 Linear Problems - Application to the Compressive Sensing Method},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={54-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006624600540062},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - A Simple and Exact Algorithm to Solve l1 Linear Problems - Application to the Compressive Sensing Method
SN - 978-989-758-290-5
AU - Ciril I.
AU - Darbon J.
AU - Tendero Y.
PY - 2018
SP - 54
EP - 62
DO - 10.5220/0006624600540062

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