期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：2
语种：English
出版社：IAENG - International Association of Engineers
摘要：In compressed sensing (CS), the traditional matching pursuit algorithms have a narrow adaptability to the sparsity and a higher time complexity. To expand the adaptability to sparsity and reduce the time complexity, a regularized-subspace pursuit (R-SP) algorithm is proposed. The regularization rule of the regularized orthogonal matching pursuit (ROMP) algorithm and the backtracing mechanism of the subspace pursuit (SP) algorithm are used to improve the accuracy of atom selection. The results of experiment show that in one-dimensional signal, the reconstruction probability of each algorithm is almost the same when the sparsity K is small. However, when the sparsity K increases, the R-SP algorithm has higher reconstruction probability and obvious advantages of reconstruction time. In two-dimensional images, the reconstruction performance of R-SP algorithm is slightly worse than ROMP algorithm. What’s more, the R-SP algorithm widens the range of sparsity K, shortens the reconstruction time and achieves the complementary advantages when compared with other algorithms.
