Abstract
Convolutional neural networks (CNNs) have been successfully applied to solve optical problems. In this paper, a method is proposed for the reconstruction and analysis of a wavefront with an irregular-shaped aperture based on deep learning, for which a U-type CNN (U-net) was used to reconstruct the wavefront image. The data generated by the simulation contain several types of wavefront images with irregularly shaped apertures for training the U-net. The results indicate that modal wavefront reconstruction of irregular-shaped apertures is feasible based on deep learning; it will be very helpful for the reconstruction and analysis of wavefronts in real time applications, and the method is robust.
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