Distorted wavefront reconstruction based on compressed sensing

The slope measured by a wavefront sensor has good sparsity in the frequency domain, so the application of compressed sensing technology to wavefront detection can significantly improve the measurement speed of the wavefront signal. In this study, the sparsity adaptive matching pursuit algorithm (SAMP) was used to reconstruct the distorted wavefront. The numerical analysis results show that, compared with the compressed sampling matching pursuit algorithm (CoSAMP) and the orthogonal matching pursuit algorithm (OMP), the reconstruction time of the SAMP algorithm is short and it has a high reconstruction accuracy. An adaptive optics system experiment was built to verify the ability of the SAMP algorithm to correct the beam wavefront distortion. The results show that after the distorted wavefront was reconstructed by the SAMP, CoSAMP, and OMP algorithms, the peak to valley values of the wavefront were reduced from 2.67 µm before correction to 0.03 µm, 0.038 µm and 0.05 µm after correction. The effectiveness of the SAMP algorithm in reconstructing a distorted wavefront was verified.