基于双阈值的压缩采样匹配追踪改进算法

Modified compressive sampling matching pursuit algorithm based on double threshold

针对基于压缩感知的压缩采样匹配追踪(CoSaMP)算法迭代次数严重依赖于信号稀疏度,候选原子冗余度大,从而导致最终的支撑原子集选择时间长、选择精度低等问题,提出一种基于双阈值的压缩采样匹配追踪算法.该算法利用模糊阈值进行支撑集候选原子的选择,引入残差与观测矩阵的相关度变化阈值作为迭代停止条件,对图像进行重构.仿真实验表明,所提出的算法重构速度快,重构效果优于CoSaMP算法.

To overcome the problems that the iterative number of compressive sampling matching pursuit（CoSaMP） algorithm is heavily dependence on sparsity K, and the larger redundancy of the candidate atoms leads to low precision, a modified CoSaMP algorithm is proposed. The algorithm reconstructs images by using fuzzy threshold to select candidate atoms for supporting set and setting the correlation threshold between measure matrix and residual error as the condition for stopping iteration. The simulations demonstrate that the modified algorithm spends less computing time than the CoSaMP algorithm, and improves the performance of the recovery.