Iterative framework for a high accuracy aberration estimation with one-shot wavefront sensing.

Deep neural networks have contributed to the progress of image-based wavefront sensing adaptive optics (AO) with the non-iterative regression of aberration. However, algorithms relying on the one-shot point spread function (PSF) typically yield less accuracy. Thus, this paper proposes an iterative closed-loop framework for wavefront aberration estimation outperforming the non-iterative baseline methods with the same computation. Specifically, we simulate the defocus PSF concerning the estimation of the Zernike coefficients and input it into the backbone network with the ground-truth defocus PSF. The difference between the ground-truth and estimated Zernike coefficients is used as a new label for training the model. The prediction updates the estimation, and the accuracy refined through iterations. The experimental results demonstrate that the iterative framework improves the accuracy of the existing networks. Furthermore, we challenge our scheme with the multi-shot phase diversity method trained with baseline networks, highlighting that the framework improves the one-shot accuracy to the multi-shot level without noise.