压缩感知技术用于大气湍流波前斜率测量能在很大程度上提高波前信号的测量速度，同时降低波前测量系统的硬件压力。与现有波前斜率测量方法不同，压缩感知波前测量方法增加了从波前斜率的稀疏测量值到波前斜率信号的重建过程，因此将压缩感知技术用于波前测量，需要快速、高精度的波前斜率重建算法。Smoothed L0 Norm (SL0)算法是一种近似L0 范数估计的优化迭代重建算法，与其它算法相比，不需要事先知道信号的稀疏度，计算量低且估计精度高。本文以SL0算法为基础，对波前斜率信号分区域测量，再结合并行运算，通过理论分析和仿真实验实现了一种能够快速、高精度重建信号的分区域并行算法—Block-Smoothed L0 Norm (B-SL0)。实验结果表明，B-SL0在计算时间和精度都明显优于现有的其它重建算法，对压缩感知技术用于大气湍流波前测量的可行性进行了初步探索。

Compressed sensing technology for atmospheric turbulence wavefront slope measurement can greatly improve the wavefront signal measurement speed, while reducing the pressure of wavefront measurement system hardware. Different from the existing wavefront slope measurement method, the compressed sensing wavefront measurement increase a process which from sparse measurement of wavefront slope value to the reconstruction of the wavefront slope signal. Therefore, a fast and accurate wavefront slope reconstruction algorithm is needed if the compressed sensing technology is used for wavefront measurement. Smoothed L0 Norm (SL0) algorithm is an optimized iterative reconstruction algorithm with approximate L0 norm estimation, and compared with other algorithms, it is not necessary to know the sparsity of the signal in advance, and the calculation is low and the estimation accuracy is high. Based on the SL0 algorithm, this paper implements a subregion parallel algorithm- Block-Smoothed L0 Norm (B-SL0) which can quickly and accurately reconstruct the signal by measuring the wavefront slope signal in subarea and parallel operations through theoretical analysis and experiments. The experimental results show that B-SL0 is significantly better than other existing reconstruction algorithms in the calculation time and accuracy, and explore the feasibility of compressed sensing technology for measurement of atmospheric turbulence wavefront preliminarily.