All-in-One image restoration aims to address multiple image degradation problems using a single model, offering a more practical and versatile solution compared to designing dedicated models for each degradation type. Existing approaches typically rely on Degradation-specific models or coarse-grained degradation prompts to guide image restoration. However, they lack fine-grained modeling of degradation information and face limitations in balancing multi-task conflicts. To overcome these limitations, we propose DPMambaIR, a novel All-in-One image restoration framework that introduces a fine-grained degradation extractor and a Degradation-Aware Prompt State Space Model (DP-SSM). The DP-SSM leverages the fine-grained degradation features captured by the extractor as dynamic prompts, which are then incorporated into the state space modeling process. This enhances the model's adaptability to diverse degradation types, while a complementary High-Frequency Enhancement Block (HEB) recovers local high-frequency details. Extensive experiments on a mixed dataset containing seven degradation types show that DPMambaIR achieves the best performance, with 27.69dB and 0.893 in PSNR and SSIM, respectively. These results highlight the potential and superiority of DPMambaIR as a unified solution for All-in-One image restoration.

翻译：一体化图像复原旨在利用单一模型解决多种图像退化问题，相比为每种退化类型设计专用模型，提供了更实用且通用的解决方案。现有方法通常依赖于退化特定模型或粗粒度退化提示来指导图像复原。然而，它们缺乏对退化信息的细粒度建模，并且在平衡多任务冲突方面存在局限性。为克服这些限制，我们提出了DPMambaIR，一种新颖的一体化图像复原框架，引入了细粒度退化提取器和退化感知提示状态空间模型（DP-SSM）。DP-SSM利用提取器捕获的细粒度退化特征作为动态提示，并将其融入状态空间建模过程。这增强了模型对不同退化类型的适应性，同时互补的高频增强模块（HEB）恢复了局部高频细节。在包含七种退化类型的混合数据集上进行的大量实验表明，DPMambaIR取得了最佳性能，PSNR和SSIM分别达到27.69dB和0.893。这些结果凸显了DPMambaIR作为一体化图像复原统一解决方案的潜力和优越性。