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.

翻译：基于扩散模型的逆问题求解器已展现出卓越性能，但受限于速度问题——这主要源于其需要从噪声出发进行反向扩散采样。近年来，多项研究通过构建针对特定逆问题的扩散过程，直接连接干净数据与损坏数据，试图缓解该问题。本文首先将现有方法统一归入"直接扩散桥"（DDB）框架，指出尽管这些方法源自不同理论，但最终算法仅在参数选择上存在差异。随后，我们揭示当前DDB框架的关键局限性：其无法确保数据一致性。为解决该问题，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，并将所得方法命名为"数据一致性直接扩散桥"（CDDB）。该方法在感知指标与失真指标上均优于非一致性版本，有效将帕累托前沿推向最优。在两个评估标准上，本方法均达到最先进水平，彰显其对现有方法的优越性。