Abstract
Diffusion models have emerged as powerful priors for solving inverse problems. However, existing approaches typically overlook side information that could significantly improve reconstruction quality, especially in severely ill-posed settings. In this work, we propose a novel inference-time search algorithm that guides the sampling process using the side information in a manner that balances exploration and exploitation. This enables more accurate and reliable reconstructions, providing an alternative to the gradient-based guidance that is prone to reward-hacking artifacts. Our approach can be seamlessly integrated into a wide range of existing diffusion-based image reconstruction pipelines. Through extensive experiments on a number of inverse problems, such as box inpainting, super-resolution, and various deblurring tasks including motion, Gaussian, nonlinear, and blind deblurring, we show that our approach consistently improves the qualitative and quantitative performance of diffusion-based image reconstruction algorithms. We also show the superior performance of our approach with respect to other baselines, including reward gradient-based guidance algorithms. The code is available at \href{this https URL}{this repository}.
Abstract (translated)
扩散模型作为解决逆问题的强大先验方法已经崭露头角。然而，现有的方法通常忽略了能够显著提高重建质量的侧面信息，尤其是在病态严重的情况下。在这项工作中，我们提出了一种新颖的推理时间搜索算法，该算法在采样过程中利用侧面信息以平衡探索与开发的方式进行引导。这使得更准确、可靠的重建成为可能，并提供了一种替代基于梯度指导的方法，后者容易出现奖励操纵伪影。我们的方法可以无缝集成到各种现有的基于扩散的图像重建管道中。通过在多个逆问题上的广泛实验（例如箱式插值、超分辨率以及包括运动模糊、高斯模糊、非线性模糊和盲去模糊在内的多种去模糊任务），我们展示了我们的方法能够持续提高基于扩散的图像重建算法的质量和量化性能。我们也证明了相对于其他基准，包括基于奖励梯度指导的方法而言，我们的方法具有优越的表现。代码可在[\href{this https URL}{此存储库}](https://this https URL)中获取。
URL
https://arxiv.org/abs/2510.03352
