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Iterative and greedy algorithms for the sparsity in levels model in compressed sensing
Author(s): Ben Adcock; Simone Brugiapaglia; Matthew King-Roskamp
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Paper Abstract

Motivated by the question of optimal functional approximation via compressed sensing, we propose generalizations of the Iterative Hard Thresholding and the Compressive Sampling Matching Pursuit algorithms able to promote sparse in levels signals. We show, by means of numerical experiments, that the proposed algorithms are successfully able to outperform their unstructured variants when the signal exhibits the sparsity structure of interest. Moreover, in the context of piecewise smooth function approximation, we numerically demonstrate that the structure promoting decoders outperform their unstructured variants and the basis pursuit program when the encoder is structure agnostic.

Paper Details

Date Published: 9 September 2019
PDF: 14 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 1113809 (9 September 2019); doi: 10.1117/12.2526373
Show Author Affiliations
Ben Adcock, Simon Fraser Univ. (Canada)
Simone Brugiapaglia, Simon Fraser Univ. (Canada)
Matthew King-Roskamp, Simon Fraser Univ. (Canada)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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