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Sparse Signals Reconstruction via Adaptive Iterative Greedy Algorithm

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 90 - Number 17
Year of Publication: 2014
Walid Osamy
Ahmed Salim
Ahmed Aziz

Walid Osamy, Ahmed Salim and Ahmed Aziz. Article: Sparse Signals Reconstruction via Adaptive Iterative Greedy Algorithm. International Journal of Computer Applications 90(17):5-11, March 2014. Full text available. BibTeX

	author = {Walid Osamy and Ahmed Salim and Ahmed Aziz},
	title = {Article: Sparse Signals Reconstruction via Adaptive Iterative Greedy Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {90},
	number = {17},
	pages = {5-11},
	month = {March},
	note = {Full text available}


Compressive sensing(CS) is an emerging research field that has applications in signal processing, error correction, medical imaging, seismology, and many more other areas. CS promises to efficiently reconstruct a sparse signal vector via a much smaller number of linear measurements than its dimension. In order to improve CS reconstruction performance, this paper present a novel reconstruction greedy algorithm called the Enhanced Orthogonal Matching Pursuit (E-OMP). E-OMP falls into the general category of Two Stage Thresholding(TST)-type algorithms where it consists of consecutive forward and backward stages. During the forward stage, E-OMP depends on solving the least square problem to select columns from the measurement matrix. Furthermore, E-OMP uses a simple backtracking step to detect the previous chosen columns accuracy and then remove the false columns at each time. From simulations it is observed that E-OMP improve the reconstruction performance better than Orthogonal Matching Pursuit (OMP) and Regularized OMP (ROMP).


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