Abstract
Computer-generated holography (CGH) has gained wide attention with deep learning-based algorithms. However, due to its nonlinear and ill-posed nature, challenges remain in achieving accurate and stable reconstruction. Specifically, ($i$) the widely used end-to-end networks treat the reconstruction model as a black box, ignoring underlying physical relationships, which reduces interpretability and flexibility. ($ii$) CNN-based CGH algorithms have limited receptive fields, hindering their ability to capture long-range dependencies and global context. ($iii$) Angular spectrum method (ASM)-based models are constrained to finite this http URL this paper, we propose a Deep Unfolding Network (DUN) that decomposes gradient descent into two modules: an adaptive bandwidth-preserving model (ABPM) and a phase-domain complex-valued denoiser (PCD), providing more flexibility. ABPM allows for wider working distances compared to ASM-based methods. At the same time, PCD leverages its complex-valued deformable self-attention module to capture global features and enhance performance, achieving a PSNR over 35 dB. Experiments on simulated and real data show state-of-the-art results.
Abstract (translated)
计算机生成全息图（CGH）由于基于深度学习的算法而受到广泛关注。然而，由于其非线性和不适定特性，实现准确且稳定的重建仍面临挑战。具体来说： (i) 广泛使用的端到端网络将重建模型视为黑盒模型，忽略了潜在的物理关系，降低了可解释性与灵活性。 (ii) 基于卷积神经网络（CNN）的CGH算法具有有限的感受野，难以捕捉长距离依赖和全局上下文信息。 (iii) 基于角谱法（ASM）的方法受到计算域限制。本文中，我们提出了一种深度展开网络（DUN），将梯度下降分解为两个模块：自适应带宽保持模型（ABPM）和相位域复值去噪器（PCD）。这种方法提供了更高的灵活性。 ABPM 允许与基于 ASM 的方法相比具有更宽的工作距离。同时，PCD 通过其复数可变形的自我注意机制来捕获全局特征并增强性能，实现了超过35dB的PSNR。在模拟和真实数据上的实验显示了最先进的结果。
URL
https://arxiv.org/abs/2508.21657
