Evolutionary algorithms for sparse signal reconstruction

• Published in: Signal, image and video processing Vol. 13; no. 7; pp. 1293 - 1301
• Main Authors: Erkoc, Murat Emre, Karaboga, Nurhan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2019; Springer Nature B.V
• Subjects: Computer Imaging; Computer Science; Detection; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Greedy algorithms; Heuristic methods; Hybrid structures; Image Processing and Computer Vision; Multimedia Information Systems; Optimization; Original Paper; Pattern Recognition and Graphics; Recovery; Signal reconstruction; Signal,Image and Speech Processing; Vision; Greedy algorithms; Differential evolution; Genetic algorithm; Sparse reconstruction; Compressed sensing
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-019-01473-w

This study includes an evolutionary algorithm technique for sparse signal reconstruction in compressive sensing. In general, l 1 minimization and greedy algorithms are used to reconstruct sparse signals. In addition to these methods, recently, heuristic algorithms have begun to be used to reconstruct sparse signals. Heuristic algorithms are used in the field of compressive sensing by creating a hybrid structure with other methods or by optimizing the problem of sparse signal reconstruction on its own. This proposed method for evolutionary algorithms has a strategy similar to the sparse signal recovery method of greedy algorithms used in compressive sensing. In addition, this method has been applied for genetic and differential evolution algorithms. Firstly, the reconstruction performance of genetic and differential evolution algorithms is compared among each other. And then, the reconstruction performance of them is compared with l 1 minimization method and greedy approaches. As a result from these studies, the proposed method for genetic and differential evolution algorithms can be used as the sparse signal recovery algorithm.