Despite substantial progress, all-in-one image restoration (IR) grapples with persistent challenges in handling intricate real-world degradations. This paper introduces MPerceiver: a novel multimodal prompt learning approach that harnesses Stable Diffusion (SD) priors to enhance adaptiveness, generalizability and fidelity for all-in-one image restoration. Specifically, we develop a dual-branch module to master two types of SD prompts: textual for holistic representation and visual for multiscale detail representation. Both prompts are dynamically adjusted by degradation predictions from the CLIP image encoder, enabling adaptive responses to diverse unknown degradations. Moreover, a plug-in detail refinement module improves restoration fidelity via direct encoder-to-decoder information transformation. To assess our method, MPerceiver is trained on 9 tasks for all-in-one IR and outperforms state-of-the-art task-specific methods across most tasks. Post multitask pre-training, MPerceiver attains a generalized representation in low-level vision, exhibiting remarkable zero-shot and few-shot capabilities in unseen tasks. Extensive experiments on 16 IR tasks underscore the superiority of MPerceiver in terms of adaptiveness, generalizability and fidelity.

翻译：尽管取得了显著进展，全场景图像修复在处理复杂真实退化时仍面临持续性挑战。本文提出MPerceiver：一种新型多模态提示学习方法，利用Stable Diffusion先验增强全场景图像修复的自适应性、泛化性与保真度。具体而言，我们开发了双分支模块以掌握两类SD提示：用于整体表征的文本提示与用于多尺度细节表征的视觉提示。两类提示均通过CLIP图像编码器的退化预测动态调整，从而实现对各类未知退化的自适应响应。此外，插件式细节精修模块通过编码器到解码器的直接信息转换提升修复保真度。为评估方法性能，MPerceiver在9项全场景图像修复任务上完成训练，并在多数任务中超越现有专用方法。经多任务预训练后，MPerceiver在低级视觉领域获得泛化表征，在未见任务中展现出显著的零样本与小样本能力。涵盖16项图像修复任务的广泛实验验证了MPerceiver在自适应性、泛化性与保真度方面的优越性。