Cross-Correlation Algorithm Based on Speeded-Up Robust Features Parallel Acceleration for Shack–Hartmann Wavefront Sensing

A cross-correlation algorithm to obtain the sub-aperture shifts that occur is a crucial aspect of scene-based SHWS (Shack–Hartmann wavefront sensing). However, when the sub-image is partially absent within the atmosphere, the traditional cross-correlation algorithm can easily obtain the wrong shift results. To overcome this drawback, we propose an algorithm based on SURFs (speeded-up-robust features) matching. In addition, to meet the speed required by wavefront sensing, CUDA parallel optimization of SURF matching is carried out using a GPU thread execution model and a programming model. The results show that the shift error can be reduced by more than two times, and the parallel algorithm can achieve nearly ten times the acceleration ratio.