%0 Journal Article
%A JIANG Tiegang
%A XU Zhiqiang
%A YANG Libo
%T Hybrid gradient based hard thresholding pursuit algorithm
%D 2020
%R 10.11772/j.issn.1001-9081.2019071296
%J Journal of Computer Applications
%P 912-916
%V 40
%N 3
%X Aiming at the problem of large number of iterations and long reconstruction time of iterative hard thresholding algorithms in Compressed Sensing (CS), a Hybrid Gradient based Hard Thresholding Pursuit (HGHTP) algorithm was proposed. Firstly, the gradient and conjugate gradient at the current iteration node were calculated in each iteration, and the support sets in the gradient domain and conjugate gradient domain were mixed and the union of these two was taken as the candidate support set for the next iteration, so that the useful information of the conjugate gradient was fully utilized in the support set selection strategy, and the support set selection strategy was optimized. Secondly, the least square method was used to perform secondary screening on the candidate support sets to quickly and accurately locate the correct support and update the sparse coefficients. The experimental results of one-dimensional random signal reconstruction show that HGHTP algorithm needs fewer iterations than the similar iterative hard thresholding algorithms on the premise of guaranteeing the success rate of reconstruction. The two-dimensional image reconstruction experimental results show that the reconstruction accuracy and anti-noise performance of HGHTP algorithm are better than those of similar iterative thresholding algorithms, and under the condition of ensuring reconstruction accuracy, HGHTP algorithm has the reconstruction time reduced by more than 32% compared with similar algorithms.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019071296