High-precision wavefront sensing for extended objects via a correlation-optical flow hybrid algorithm.

In extended object wavefront sensing for astronomical high-resolution imaging and medical microscopy imaging, the object's intrinsic structure violates the conventional point-source assumption, limiting the accuracy of existing methods. To address this, we propose a hybrid wavefront sensing algorithm combining cross-correlation and optical flow estimation: an enhanced sum-of-squared-differences function for global coarse positioning, a physically constrained optical flow model for subpixel fine estimation, and an adaptive template update to control computational cost. Compared to conventional matching methods, this hierarchical hybrid framework simultaneously balances robustness and high-precision requirements. Numerical simulations and experimental results jointly demonstrate that the proposed algorithm outperforms comparative algorithms in robustness and accuracy under strong turbulence, complex structures, and noisy environments. The experimental reconstruction residual peak-to-valley and root mean square values are 0.3559λ and 0.0511λ, respectively, meeting real-time requirements and providing an effective solution for complex extended-scene wavefront sensing.