Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Abstract

Due to the influence of atmospheric turbulence, the detector, and background noise, the subaperture image of an extended scene Shack–Hartmann wavefront sensor will have a low signal-to-noise ratio, which will introduce errors to the offset estimation and reduce the accuracy of the slope measurement. To solve this problem, this paper proposes a cross-correlation subaperture image preprocessing method, which uses the generalized Anscombe transform to convert the Gauss–Poisson noise into Gaussian noise and introduces residual feedback on the basis of BM3D to achieve the efficient denoising of subaperture images. The simulation results show that compared with the three commonly used denoising algorithms, the proposed method improves the relative error of the subaperture offset calculation by 51.96% and the corresponding Zernike coefficient of distorted reconstruction wavefront by 85.56%, which realizes the improvement in the detection accuracy on the basis of effectively retaining image details.