Denoising diffusion models have emerged as the go-to framework for solving inverse problems in imaging. A critical concern regarding these models is their performance on out-of-distribution (OOD) tasks, which remains an under-explored challenge. Realistic reconstructions inconsistent with the measured data can be generated, hallucinating image features that are uniquely present in the training dataset. To simultaneously enforce data-consistency and leverage data-driven priors, we introduce a novel sampling framework called Steerable Conditional Diffusion. This framework adapts the denoising network specifically to the available measured data. Utilising our proposed method, we achieve substantial enhancements in OOD performance across diverse imaging modalities, advancing the robust deployment of denoising diffusion models in real-world applications.

翻译：去噪扩散模型已成为解决成像逆问题的首选框架。这类模型在分布外任务上的性能仍是一个尚未充分探索的关键挑战。模型可能生成与测量数据不一致的逼真重建结果，虚构出训练数据集中独有的图像特征。为了同时确保数据一致性与利用数据驱动先验，我们提出了一种新颖的采样框架——可导向条件扩散。该框架使去噪网络能够专门适应可用的测量数据。通过所提出的方法，我们在多种成像模态下显著提升了分布外任务性能，推动了去噪扩散模型在真实世界应用中的稳健部署。