Exploiting pre-trained diffusion models for restoration has recently become a favored alternative to the traditional task-specific training approach. Previous works have achieved noteworthy success by limiting the solution space using explicit degradation models. However, these methods often fall short when faced with complex degradations as they generally cannot be precisely modeled. In this paper, we propose PGDiff by introducing partial guidance, a fresh perspective that is more adaptable to real-world degradations compared to existing works. Rather than specifically defining the degradation process, our approach models the desired properties, such as image structure and color statistics of high-quality images, and applies this guidance during the reverse diffusion process. These properties are readily available and make no assumptions about the degradation process. When combined with a diffusion prior, this partial guidance can deliver appealing results across a range of restoration tasks. Additionally, PGDiff can be extended to handle composite tasks by consolidating multiple high-quality image properties, achieved by integrating the guidance from respective tasks. Experimental results demonstrate that our method not only outperforms existing diffusion-prior-based approaches but also competes favorably with task-specific models.

翻译：利用预训练扩散模型进行图像恢复，近来已成为传统任务特定训练方法的优选替代方案。以往的工作通过显式退化模型限定解空间取得了显著成功。然而，这些方法在面对复杂退化时往往表现不足，因为此类退化通常难以精确建模。本文提出PGDiff，引入部分引导这一全新视角，与现有工作相比，该方法更适应真实世界中的退化场景。我们的方法不具体定义退化过程，而是对高质量图像的理想属性（如图像结构和色彩统计）进行建模，并在反向扩散过程中应用此类引导。这些属性易于获取且不对退化过程做任何假设。当与扩散先验结合时，这种部分引导能在多种恢复任务中产生令人满意的结果。此外，通过整合各任务对应的引导，PGDiff可扩展至复合任务的处理——将多个高质量图像属性统一结合。实验结果表明，我们的方法不仅优于现有基于扩散先验的方法，还能与任务特定模型竞争。