Abstract
The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error.
Highlights
Exploiting the sparse nature of the signals is highly challenging in various signal processing applications such as signal compression, inverse problems and this motivated the development of compressive sensing (CS) methodologies (Donoho, 2006)
The MATLAB environment is used for performing all simulations and the reconstruction is investigated by Gaussian matrix F, of size M Â N, where M = 128 and N = 256
The results prove that grey wolf reconstruction algorithm (GWRA) algorithm still gives the lowest average normalized mean squared error (ANMSE) value than compressive sampling matching pursuit (CoSaMP), filtered back projection (FBP), orthogonal matching pursuit (OMP), sum product (SP), BA, PSO as K > 53, K ! 25, K > 20, K > 26, K > 33, K ! 45, K > 37, respectively, because what any greedy algorithms (GAs) does in one round, GWRA does it for each search agent and it selects the best one in every iteration to converge at the optimal solution
Summary
Exploiting the sparse nature of the signals is highly challenging in various signal processing applications such as signal compression, inverse problems and this motivated the development of compressive sensing (CS) methodologies (Donoho, 2006). CS provides an alternative new method of compressing data, offering a new signal sampling theory which we can adopt in variety of applications including data and sensor networks (Cevher & Jafarpour, 2010), medical systems, image processing and video camera, signal detection, analog-to-digital convertors (Choi et al, 2010) and several other applications. The CS reconstruction problems are solved by convex algorithms and greedy algorithms (GAs). Convex algorithms are not efficient because they require high complex computations. Most of researchers choose GAs, which are faster and give the same performance as convex algorithms in terms of minimum reconstruction error.
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