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DPMambaIR:All-in-One Image Restoration via Degradation-Aware Prompt State Space Model

All-in-One image restoration aims to address multiple image degradation problems using a single model, significantly reducing training costs and deployment complexity compared to traditional methods that design 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. By integrating a Degradation-Aware Prompt State Space Model (DP-SSM) and a High-Frequency Enhancement Block (HEB), DPMambaIR enables fine-grained modeling of complex degradation information and efficient global integration, while mitigating the loss of high-frequency details caused by task competition. Specifically, the DP-SSM utilizes a pre-trained degradation extractor to capture fine-grained degradation features and dynamically incorporates them into the state space modeling process, enhancing the model's adaptability to diverse degradation types. Concurrently, the HEB supplements high-frequency information, effectively addressing the loss of critical details, such as edges and textures, in multi-task image restoration scenarios. 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.

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