DWS-Net: a depth-wise separable convolutional neural network for robust phase-only hologram encoding

Neural-network-based computer-generated hologram (CGH) methods have greatly improved computational efficiency and reconstruction quality.However, they will no longer be suitable when the CGH parameters change.A common phase-only hologram (POH) encoding strategy based on a neural network is presented to encode POHs from complex amplitude holograms for different parameters in a single training.The neural network is built up by introducing depth-wise separable convolution, residual modules, and complex-value channel adaptive modules, and it is randomly trained with traditional training input image datasets, a built wavelength pool, and a reconstructed distance pool.The average peak signal-to-noise ratio/structural similarity index measure (PSNR/SSIM) for the proposed network encoded POHs can reach 29.19 dB/0.83, which shows 117.35% and 144.12% improvement compared with the double-phase encoding strategy.The variances of the PSNR and SSIM in different reconstructed distances and different wavelengths are increased by 86.83% and 80.65%, respectively, compared with traditional networks in this strategy.Such a method makes it possible to encode multiple complex amplitude holograms of arbitrary wavelength and arbitrary reconstructed distance without the need for retraining, which is friendly to digital filtering or other operations within the CGH generation process for CGH designing and CGH debugging.