标题： 移动全息：一种用于高质量计算机生成全息图的轻量级复值可变形CNN 显示英文标题

标题： MobileHolo: A Lightweight Complex-Valued Deformable CNN for High-Quality Computer-Generated Hologram

摘要： 全息显示在虚拟现实和增强现实领域具有重要意义，因为它们能够提供所有深度线索。基于深度学习的方法在计算机生成全息图（CGH）中起着重要作用。在衍射过程中，每个像素都会影响重建图像。然而，先前的工作在捕捉足够信息以准确模拟此过程时面临挑战，主要是由于其有效感受野（ERF）的不足。在这里，我们设计了复值可变形卷积以集成到网络中，使卷积核形状动态调整，从而增加ERF的灵活性以更好地提取特征。这种方法允许我们在使用单个模型的同时，在模拟和光学实验重建中达到最先进的性能，超越现有的开源模型。具体而言，当分辨率为1920$\times$1072时，我们的方法的峰值信噪比分别比CCNN-CGH、HoloNet和Holo-encoder高2.04 dB、5.31 dB和9.71 dB。我们的模型参数数量仅为CCNN-CGH的八分之一左右。 显示英文摘要

摘要： Holographic displays have significant potential in virtual reality and augmented reality owing to their ability to provide all the depth cues. Deep learning-based methods play an important role in computer-generated holograms (CGH). During the diffraction process, each pixel exerts an influence on the reconstructed image. However, previous works face challenges in capturing sufficient information to accurately model this process, primarily due to the inadequacy of their effective receptive field (ERF). Here, we designed complex-valued deformable convolution for integration into network, enabling dynamic adjustment of the convolution kernel's shape to increase flexibility of ERF for better feature extraction. This approach allows us to utilize a single model while achieving state-of-the-art performance in both simulated and optical experiment reconstructions, surpassing existing open-source models. Specifically, our method has a peak signal-to-noise ratio that is 2.04 dB, 5.31 dB, and 9.71 dB higher than that of CCNN-CGH, HoloNet, and Holo-encoder, respectively, when the resolution is 1920$\times$1072. The number of parameters of our model is only about one-eighth of that of CCNN-CGH.