Wavefront Distortion Correction for the Non-Uniform-Intensity Light Based on Extreme Learning Machine

This paper aims to employ a neural network method of extreme learning machine to solve the problem of laser beam cleanup under the dynamic non-uniform-intensity distribution. This neural network is used to fit the corresponding nonlinear relationship between the slope information detected by the wavefront sensor and the recovery voltage of deformable mirror under the dynamic non-uniform-intensity distribution. The simulation results show that the beam quality after correction is improved from 1.7 to 1.3 times diffraction limit. And the residual wavefront after correction for the proposed method has been reduced by 55%, while the direct gradient method is 17%.