Diffusion model-based inverse problem solvers have shown impressive performance, but are limited in speed, mostly as they require reverse diffusion sampling starting from noise. Several recent works have tried to alleviate this problem by building a diffusion process, directly bridging the clean and the corrupted for specific inverse problems. In this paper, we first unify these existing works under the name Direct Diffusion Bridges (DDB), showing that while motivated by different theories, the resulting algorithms only differ in the choice of parameters. Then, we highlight a critical limitation of the current DDB framework, namely that it does not ensure data consistency. To address this problem, we propose a modified inference procedure that imposes data consistency without the need for fine-tuning. We term the resulting method data Consistent DDB (CDDB), which outperforms its inconsistent counterpart in terms of both perception and distortion metrics, thereby effectively pushing the Pareto-frontier toward the optimum. Our proposed method achieves state-of-the-art results on both evaluation criteria, showcasing its superiority over existing methods. Code is available at https://github.com/HJ-harry/CDDB

翻译：基于扩散模型的逆问题求解器性能优异，但受限于计算速度——这主要源于它们需要从噪声开始进行反向扩散采样。近期多项研究试图通过构建特定逆问题的扩散过程，直接桥接干净数据与退化数据来解决此问题。本文首先将这些现有工作统一命名为直接扩散桥接（DDB），并证明尽管它们基于不同理论，但最终算法仅在参数选择上存在差异。随后我们指出现有DDB框架的关键缺陷：无法保证数据一致性。为解决此问题，我们提出一种无需微调即可施加数据一致性的改进推理流程，将新方法命名为数据一致性直接扩散桥接（CDDB）。与缺乏数据一致性的对应方法相比，CDDB在感知指标与失真指标上均表现更优，有效推动帕累托前沿向最优解逼近。所提方法在两个评估标准上均达到当前最优水平，展现出对现有方法的显著优势。代码开源地址：https://github.com/HJ-harry/CDDB