Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

A real-time wavefront sensing method for arbitrary targets is proposed, which provides an effective way for diversified wavefront sensing application scenarios. By using a distorted grating, the positive and negative defocus images are simul-taneously acquired on a single detector. A fine feature, which is independent of the target itself but corresponding to the wavefront aberration, is defined. A lightweight and efficient network combined with an attention mechanism (AM-EffNet) is proposed to establish an accurate mapping between the features and the incident wavefronts. Comparison results show that the proposed method has superior performance compared to other methods and can achieve high-accuracy wavefront sensing in varied target scenes only by using the point target dataset to train the network well.