基于均值阈值和回溯策略的SWOMP重构算法
SWOMP RECONSTRUCTION ALGORITHM BASED ON MEAN THRESHOLD AND BACKTRACKING STRATEGY
李忠兵 1赵茂君 1谌贵辉 1庞微1
作者信息
- 1. 西南石油大学电气信息学院 四川成都 610500
- 折叠
摘要
为提高压缩感知重建算法中阶段性弱选择正交匹配追踪(SWOMP)算法的重建精度和运行速度,提出一种基于均值阈值和回溯策略的SWOMP算法.该算法利用均值策略自适应选择原子,提高了原子筛选的精确性;采用回溯策略对所选原子进行二次筛选,优化支撑集提高算法的重建精度;通过简化矩阵的设计减少算法迭代次数,提高了算法的运行速度.仿真实验表明,该算法对一维随机信号和二维图像信号的重构性能明显优于其他同类算法,具有重建精度高、用时少的特点.
Abstract
In order to improve the reconstruction accuracy and running speed of the staged weak selection orthogonal matching pursuit(SWOMP)algorithm in the compressed sensing reconstruction algorithm,a SWOMP algorithm based on the mean threshold and backtracking strategy is proposed.The algorithm used the mean strategy to adaptively select atoms,which improved the accuracy of atomic screening.We used the backtracking strategy to perform secondary screening on the selected atoms,and optimized the support set to improve the reconstruction accuracy of the algorithm.The matrix design was simplified to reduce the iteration times of the algorithm,which increased the running speed of the algorithm.Simulation experiments show that the reconstruction performance of this algorithm for one-dimensional random signals and two-dimensional image signals is significantly better than other similar algorithms,and it has the characteristics of high reconstruction accuracy and less time consumption.
关键词
压缩感知/阶段性弱选择正交匹配追踪/稀疏重建/贪婪算法/回溯/均值策略Key words
Compressed sensing/Staged weak selection orthogonal matching tracking/Sparse reconstruction/Greedy algorithm/Backtracking/Mean strategy引用本文复制引用
基金项目
西南石油大学启航计划(2015QH027)
出版年
2024
国家科技期刊平台
NSTL
万方数据