Object-independent image-based wavefront sensing approach using phase diversity images and deep learning.

This paper proposes an image-based wavefront sensing approach using deep learning, which is applicable to both point source and any extended scenes at the same time, while the training process is performed without any simulated or real extended scenes. Rather than directly recovering phase information from image plane intensities, we first extract a special feature in the frequency domain that is independent of the original objects but only determined by phase aberrations (a pair of phase diversity images is needed in this process). Then the deep long short-term memory (LSTM) network (a variant of recurrent neural network) is introduced to establish the accurate non-linear mapping between the extracted feature image and phase aberrations. Simulations and an experiment are performed to demonstrate the effectiveness and accuracy of the proposed approach. Some other discussions are further presented for demonstrating the superior non-linear fitting capacity of deep LSTM compared to Resnet 18 (a variant of convolutional neural network) specifically for the problem encountered in this paper. The effect of the incoherency of light on the accuracy of the recovered wavefront phase is also quantitatively discussed. This work will contribute to the application of deep learning to image-based wavefront sensing and high-resolution image reconstruction.