Title of article
Compare the Performance of Recovery Algorithms MP, OMP, L1-Norm in Compressive Sensing for Different Measurement and Sparse Spaces
Author/Authors
Davoodi, Bahareh Electrical Engineering Department - South Tehran Branch - Islamic Azad University, Tehran , Ghofrani, Sedigheh Electrical Engineering Department - South Tehran Branch - Islamic Azad University, Tehran
Pages
6
From page
21
To page
26
Abstract
In this paper, at first, compressive sensing theory involves introducing measurement matrices to dedicate the signal dimension and so sensing cost reduction, and sparse domain to exam-ine the conditions for the possibility of signal recovering, are explained. In addition, three well known recovery algorithms called Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), and L1-Norm are briefly introduced. Then, the performance of three mentioned re-covery algorithms are compared with respect to the mean square error (MSE) and the result images quality. For this purpose, Gaussian and Bernoulli as the measurement matrices are used, where Haar and Fourier as sparse domains are applied.
Keywords
Matching Pursuit , Orthogonal Matching Pursuit , Compressive Sensing , Sparse Space
Journal title
Astroparticle Physics
Serial Year
2017
Record number
2432870
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