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On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery

Abstract: 

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. Here, we first propose the Signal space Subspace Pursuit (SSSP) algorithm , and then we derive a low bound on the number of measurements required. The algorithm has low computational complexity and provides high recovery accuracy.

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Submitted On:
4 November 2019 - 10:52pm
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Presenter's Name:
Zhu Jianchen

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GlobalSIP 2019 jianchen zhu(1)(1).pdf

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[1] , "On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4911. Accessed: Feb. 18, 2020.
@article{4911-19,
url = {http://sigport.org/4911},
author = { },
publisher = {IEEE SigPort},
title = {On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery},
year = {2019} }
TY - EJOUR
T1 - On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4911
ER -
. (2019). On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery. IEEE SigPort. http://sigport.org/4911
, 2019. On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery. Available at: http://sigport.org/4911.
. (2019). "On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery." Web.
1. . On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4911