提出残差扩散桥模型，精准修复图像退化区域并保护未受损部分。
Residual Diffusion Bridge Model for Image Restoration
- 基于残差机制动态调节噪声注入与去除，实现自适应修复。
- 在多种图像修复任务中达到当前最优性能，定量指标显著领先。
- 理论证明现有桥模型均为其特例，为统一建模提供新视角。
扩散桥模型在任意配对分布间建立概率路径，在通用图像修复中展现出巨大潜力。然而，现有方法多将其视为随机插值的简单变体，缺乏统一的分析视角；同时，通过全局噪声注入与移除进行图像重建，不可避免地破坏未退化的区域。为此，本文提出残差扩散桥模型（RDBM）。我们从理论上重新推导广义扩散桥的随机微分方程，得到前向与逆向过程的解析表达式。关键在于，利用原始分布间的残差信息调控噪声注入与移除过程，实现退化区域的自适应修复，同时保留完好区域。此外，我们揭示了现有桥模型的根本数学本质——均为RDBM的特例，并通过实验证明所提模型的最优性。大量实验表明，该方法在多种图像修复任务中均取得先进水平的定性与定量表现。代码已公开于https://github.com/MiliLab/RDBM。
原文摘要 · Abstract (English)
Diffusion bridge models establish probabilistic paths between arbitrary paired distributions and exhibit great potential for universal image restoration. Most existing methods merely treat them as simple variants of stochastic interpolants, lacking a unified analytical perspective. Besides, they indiscriminately reconstruct images through global noise injection and removal, inevitably distorting undegraded regions due to imperfect reconstruction. To address these challenges, we propose the Residual Diffusion Bridge Model (RDBM). Specifically, we theoretically reformulate the stochastic differential equations of generalized diffusion bridge and derive the analytical formulas of its forward and reverse processes. Crucially, we leverage the residuals from given distributions to modulate the noise injection and removal, enabling adaptive restoration of degraded regions while preserving intact others. Moreover, we unravel the fundamental mathematical essence of existing bridge models, all of which are special cases of RDBM and empirically demonstrate the optimality of our proposed models. Extensive experiments are conducted to demonstrate the state-of-the-art performance of our method both qualitatively and quantitatively across diverse image restoration tasks. Code is publicly available at https://github.com/MiliLab/RDBM.
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