In this paper, we aim to solve complex real-world image restoration situations, in which, one image may have a variety of unknown degradations. To this end, we propose an all-in-one image restoration framework with latent diffusion (AutoDIR), which can automatically detect and address multiple unknown degradations. Our framework first utilizes a Blind Image Quality Assessment Module (BIQA) to automatically detect and identify the unknown dominant image degradation type of the image. Then, an All-in-One Image Refinement (AIR) Module handles multiple kinds of degradation image restoration with the guidance of BIQA. Finally, a Structure Correction Module (SCM) is proposed to recover the image details distorted by AIR. Our comprehensive evaluation demonstrates that AutoDIR outperforms state-of-the-art approaches by achieving superior restoration results while supporting a wider range of tasks. Notably, AutoDIR is also the first method to automatically handle real-scenario images with multiple unknown degradations.

翻译：本文旨在解决复杂的真实世界图像修复问题，其中单张图像可能包含多种未知退化类型。为此，我们提出了一种基于潜扩散的全自动一体化图像修复框架（AutoDIR），该框架能够自动检测并处理多种未知退化。框架首先利用盲图像质量评估模块（BIQA）自动检测并识别图像的主要未知退化类型；随后，一体化图像精炼模块（AIR）在BIQA的引导下处理多种退化类型的图像修复任务；最后，我们提出结构校正模块（SCM）以恢复因AIR处理而失真的图像细节。综合评估表明，AutoDIR在支持更广泛任务的同时，实现了优于现有最优方法的修复效果。值得注意的是，AutoDIR是首个能够自动处理具有多种未知退化的真实场景图像的方法。