Compare Performance of Recovery Algorithms MP, OMP, L1-Norm in Compressive Sensing for Different Measurement and Sparse Spaces
Publish place: Signal Processing and Renewable Energy، Vol: 1، Issue: 3
Publish Year: 1396
Type: Journal paper
Language: English
View: 522
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Document National Code:
JR_SPRE-1-3_003
Index date: 14 July 2019
Compare Performance of Recovery Algorithms MP, OMP, L1-Norm in Compressive Sensing for Different Measurement and Sparse Spaces 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 examine 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 recovery 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.
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Compare Performance of Recovery Algorithms MP, OMP, L1-Norm in Compressive Sensing for Different Measurement and Sparse Spaces authors
Bahareh Davoodi
Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran
Sedigheh Ghofrani
Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran