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 simultaneously 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.

基于光学成像的流场测量技术，如粒子图像测速技术（PIV），易受到因流体中折射率的不均匀性或晃动的介质边界引起的光学畸变而带来的影响。这些畸变会使得示踪粒子在图像上的位置分布产生误差且严重影响图像清晰度，从而增大流场速度测量的误差。为了提高光学流场速度测量的测量精度，自适应光学系统可以应用于其中去校正光学畸变。基于图像流场测量中的光学像差具有频率高，动态范围大，空间分辨率高等特点，对于这一应用场景，基于波前校正器件的自适应光学系统受到了器件本身性能的影响。基于深度学习的自适应光学技术在流场测量中的应用，建立了一种基于深度神经网络的无波前校正器件自适应光学校正技术，以深度神经网络代替传统的波前校正器件，用于粒子图像测速技术中的光学畸变校正。为了生成神经网络所需要的训练和测试数据集，搭建了可以实现波前测量的粒子图像测速实验平台，分析并建立了光学畸变在粒子图像上的图像退化模型。最后，以校正后PIV图像的校正效果和流场速度测量结果作为评价标准，对所建立神经网络的畸变校正性能进行了分析。