一种压缩感知重构算法

为提高压缩感知重构精度，该文提出一种分段弱阈值修正共轭梯度追踪算法。该算法修正了方向追踪算法的方向，明确给出了搜寻原子下标的停止迭代准则，利用搜寻所得下标集通过最小二乘法得到稀疏信号的估计值。仿真结果表明在同等稀疏的条件下实现精确重构，该算法与匹配追踪(MP)算法和分段正交匹配追踪FDR阈值算法(StOMP-FDR)相比,所需的观测值个数少20%；在处理2维图像信号时，其重构精度比分段正交匹配追踪FAR阈值算法(StOMP-FAR)和贝叶斯算法(BCS)高1%。

A Recovery -Algorithm for Compressed Sensing

In order to improve recovery accuracy for compressed sensing, a Stagewise Weak selection Modifying approximation Conjugate Gradient Pursuit (StWMCGP) algorithm is proposed in this paper. This algorithm modifies the direction in the directional pursuit algorithm and clearly presents a stopping criterion to search the indices of elements and get a set. Then the evaluation of sparse signal is obtained by using Least-squares algorithm and the set. Simulated results show that for the same sparsity level, the number of measurements needed by the algorithm is about 20% less than that needed by MP or StOMP-FDR to exactly recover. When recovering two-dimensional image signal, the recovery accuracy of this algorithm is about 1% higher than that of BCS or StOMP-FAR.