Phase dual-resolution networks for a computer-generated hologram

Abstract

The computer-generated hologram (CGH) is a method for calculating arbitrary optical field interference patterns. Iterative algorithms for CGHs require a built-in trade-off between computation speed and accuracy of the hologram, which restricts the performance of applications. Although the non-iterative algorithm for CGHs is quicker, the hologram accuracy does not meet expectations. We propose a phase dual-resolution network (PDRNet) based on deep learning for generating phase-only holograms with fixed computational complexity. There are no ground-truth holograms employed in the training; instead, the differentiability of the angular spectrum method is used to realize unsupervised training of the convolutional neural network. In the PDRNet algorithm, we optimized the dual-resolution network as the prototype of the hologram generator to enhance the mapping capability. The combination of multi-scale structural similarity (MS-SSIM) and mean square error (MSE) is used as the loss function to generate a high-fidelity hologram. The simulation indicates that the proposed PDRNet can generate high-fidelity 1080P resolution holograms in 57 ms. Experiments in the holographic display show fewer speckles in the reconstructed image.