Complex-valued attention feature distillation network for high-fidelity phase-only hologram generation

In this paper, we propose a complex-valued attention feature distillation network (CAFDN) that incorporates a novel lightweight module (CAFDN_Lite) within dual U-Net architectures for phase-only hologram (POH) generation. The proposed network architecture employs simplified upsampling and downsampling layers to enhance computational efficiency, while the CAFDN_Lite module implements hierarchical feature extraction through integrated local attention mechanisms, multi-scale analysis, and channel pruning. This synergistic design enables progressive feature refinement across successive network layers, achieving optimal balance between representational capacity and computational efficiency through systematic feature distillation. The proposed method achieves an average peak signal-to-noise ratio (PSNR) of 32.52 dB and an average structural similarity index (SSIM) of 0.861 within a running time of 36 ms, outperforming conventional approaches. Both numerical reconstructions and optical experiments confirm superior detail reproduction and image quality while reducing computational demands. These advancements highlight the framework's potential for practical holographic display applications, especially in real-time and high-fidelity reconstruction scenarios.