用可学习的分数阶模型提升屏下ToF成像精度
Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyond
- 结合神经网络与物理建模，用分数阶反应-扩散机制迭代优化深度图
- 在四个数据集上显著改善深度质量，有效抑制信号衰减与多路径干扰
- 适合做屏下传感器、动态光场成像等需要高精度深度感知的研究
屏下ToF成像旨在通过屏幕下方的ToF相机实现精确深度感知。然而，透明OLED（TOLED）层会引入严重退化，如信号衰减、多路径干扰（MPI）和时间噪声，显著影响深度质量。为缓解此问题，我们提出可学习分数阶反应-扩散动力学（LFRD2），一种融合神经网络表达能力与物理建模可解释性的混合框架。具体而言，我们设计了时间分数阶反应-扩散模块，通过动态生成微分阶数实现迭代深度优化，捕捉长期依赖关系。此外，引入基于系数预测与重复微分的高效连续卷积算子，进一步提升恢复质量。在四个基准数据集上的实验验证了该方法的有效性。代码已公开于 https://github.com/wudiqx106/LFRD2。
原文摘要 · Abstract (English)
Under-display ToF imaging aims to achieve accurate depth sensing through a ToF camera placed beneath a screen panel. However, transparent OLED (TOLED) layers introduce severe degradations-such as signal attenuation, multi-path interference (MPI), and temporal noise-that significantly compromise depth quality. To alleviate this drawback, we propose Learnable Fractional Reaction-Diffusion Dynamics (LFRD2), a hybrid framework that combines the expressive power of neural networks with the interpretability of physical modeling. Specifically, we implement a time-fractional reaction-diffusion module that enables iterative depth refinement with dynamically generated differential orders, capturing long-term dependencies. In addition, we introduce an efficient continuous convolution operator via coefficient prediction and repeated differentiation to further improve restoration quality. Experiments on four benchmark datasets demonstrate the effectiveness of our approach. The code is publicly available at https://github.com/wudiqx106/LFRD2.
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