Recently, significant breakthroughs have been made in all-in-one image restoration (AiOIR), which can handle multiple restoration tasks with a single model. However, existing methods typically focus on a specific image domain, such as natural scene, medical imaging, or remote sensing. In this work, we aim to extend AiOIR to multiple domains and propose the first multi-domain all-in-one image restoration method, DATPRL-IR, based on our proposed Domain-Aware Task Prompt Representation Learning. Specifically, we first construct a task prompt pool containing multiple task prompts, in which task-related knowledge is implicitly encoded. For each input image, the model adaptively selects the most relevant task prompts and composes them into an instance-level task representation via a prompt composition mechanism (PCM). Furthermore, to endow the model with domain awareness, we introduce another domain prompt pool and distill domain priors from multimodal large language models into the domain prompts. PCM is utilized to combine the adaptively selected domain prompts into a domain representation for each input image. Finally, the two representations are fused to form a domain-aware task prompt representation which can make full use of both specific and shared knowledge across tasks and domains to guide the subsequent restoration process. Extensive experiments demonstrate that our DATPRL-IR significantly outperforms existing SOTA image restoration methods, while exhibiting strong generalization capabilities. Code is available at https://github.com/GuangluDong0728/DATPRL-IR.

翻译：近年来，一体化图像复原（AiOIR）领域取得了重大突破，其能够使用单一模型处理多种复原任务。然而，现有方法通常专注于特定的图像领域，例如自然场景、医学成像或遥感。在本工作中，我们旨在将AiOIR扩展到多个领域，并基于我们提出的领域感知任务提示表示学习，提出了首个多领域一体化图像复原方法DATPRL-IR。具体而言，我们首先构建一个包含多个任务提示的任务提示池，其中隐式编码了与任务相关的知识。对于每个输入图像，模型通过提示组合机制自适应地选择最相关的任务提示，并将其组合成实例级的任务表示。此外，为了使模型具备领域感知能力，我们引入了另一个领域提示池，并将来自多模态大语言模型的领域先验知识蒸馏到领域提示中。PCM被用来将自适应选择的领域提示组合成每个输入图像的领域表示。最后，这两种表示被融合以形成一个领域感知的任务提示表示，该表示能够充分利用跨任务和跨领域的特定知识与共享知识来指导后续的复原过程。大量实验表明，我们的DATPRL-IR显著优于现有的SOTA图像复原方法，同时展现出强大的泛化能力。代码可在https://github.com/GuangluDong0728/DATPRL-IR获取。