NDLI: Comparative analysis of sparse signal recovery algorithms based on minimization norms

Content Provider	IEEE Xplore Digital Library
Author	Haider, Hassaan Shah, Jawad Ali Ali, Usman
Copyright Year	2014
Description	Author affiliation: Department of Electronic Engineering, Faculty of Engineering and Technology, International Islamic University, Islamabad, Pakistan (Shah, Jawad Ali) \|\| Department of Electrical Engineering, College of Electrical and Mechanical Engineering, National University of Science and Technology, Pakistan (Haider, Hassaan; Ali, Usman)
Abstract	In conventional sensing modality, Nyquist sampling theorem is followed as the minimum sampling rate. However, due to constraints e.g. slow sampling process, limited memory, and sensors cost, in some applications Nyquist sampling rate is difficult to achieve. When sampling rate is less than Nyquist sampling rate, aliasing artifacts occur in the recovered signal. Compressed Sensing (CS) is a modern sampling technique, where signal can be recovered faithfully even from fewer samples if signal/image of interest is sparse, which true as most signals/images are sparse in appropriate domain i.e. Wavelet transform, finite difference. Recovering sparse signal efficiently from compressively sampled data can be most challenging part in CS. The recovery problem is highly ill-posed underdetermined system of linear equations, so additional regularization constraints are required. As there can be infinite many solutions, therefore, finding best solution from few measurements becomes an optimization problem, where cost function is minimized. There are several reconstruction methods that exist in literature. These methods can be classified, based on the norms that are used in minimizing the objective function. This paper presents a comparati ve study of modern sparse signal recovery algorithms using different norms. Sparse signal recovery algorithms presented in this paper are Smoothed l0, l1 magic and mixed l1l2 norm based Iterative Shrinkage Algorithms (ISA) e.g. SSF, IRLS and PCD. All algorithms are tested for the recovery of sparse image. The performance measures used for objectively analysing the efficiency of algorithms are mean square error, correlation and computational time.
Starting Page	72
Ending Page	76
File Size	697804
Page Count	5
File Format	PDF
ISBN	9781908320445
DOI	10.1109/WCST.2014.7030100
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-08
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Infonomics Society
Subject Keyword	Correlation Random access memory Inverse Problem Compressed Sensing Programming Norms Linear programming Time measurement Sensors Read only memory Sparse Signal Recovery
Content Type	Text
Resource Type	Article

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