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Proceedings Paper

Soft recovery
Author(s): Axel Flinth
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Paper Abstract

This article provides a new type of analysis of a compressed-sensing based technique for recovering column-sparse matrices, namely minimization of the l1,2-norm. Rather than providing conditions on the measurement matrix which guarantees the solution of the program to be exactly equal to the ground truth signal (which already has been thoroughly investigated), it presents a condition which guarantees that the solution is approximately equal to the ground truth. Soft recovery statements of this kind are to the best knowledge of the author a novelty in Compressed Sensing.

Paper Details

Date Published: 24 August 2017
PDF: 17 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940U (24 August 2017); doi: 10.1117/12.2272132
Show Author Affiliations
Axel Flinth, Technische Univ. Berlin (Germany)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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