Multi-scale digital holographic reconstruction with deep learning

Aimed at the problems of complex operation and zero-order influence in the reconstruction of holograms of multi-scale and large objects, a hologram reconstruction method based on deep learning was proposed. The UHC-Net model combined with the ConvNeXt module and hierarchical polymerization HANC module was adopted as the network structure to achieve accurate reconstruction of holograms. Using analog holograms and actual digital holograms as mixed data sets for reconstruction experiments, the generalization of the network can be extended. The results show that the proposed method can improve the reconstruction speed while maintaining the resolution and accuracy of reconstructed images. The results of this study can be applied to the reconstruction of large-size holograms, and the network generalization ability can be improved by using an analog data set, which provides a useful reference for the application of digital holography detection and deep learning in optical imaging.