一个模型搞定七种图像退化问题，性能领先。
DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model
- 用细粒度退化特征生成动态提示，指导修复过程
- 在混合数据集上达27.69dB PSNR、0.893 SSIM
- 适合需要统一修复多种退化的实际应用场景
全功能图像修复旨在用单一模型解决多种图像退化问题，相比为每类退化设计专用模型更具实用性和灵活性。现有方法通常依赖退化特异性模型或粗粒度退化提示，缺乏对退化信息的精细建模，并面临多任务冲突的平衡难题。为此，我们提出DPMambaIR，一种新型全功能图像修复框架，引入细粒度退化提取器与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，融入状态空间建模过程，提升模型对多样化退化类型的适应能力；同时，互补的高频增强模块（HEB）恢复局部高频细节。在包含七种退化类型的混合数据集上，DPMambaIR取得最佳性能，PSNR达27.69dB，SSIM为0.893。结果表明，DPMambaIR作为统一解决方案具有显著潜力与优势。
原文摘要 · Abstract (English)
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.
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