检索即复原：基于检索式退化库的高效一体化图像复原
Retrieve-to-Restore: Efficient All-in-One Image Restoration with a Retrieval-Based Degradation Bank
把退化知识外置成一个可检索的先验库，用退化匹配在推理时调制骨干网络特征，为多退化统一复原提供了一条解耦「退化适应」与「重建能力」的轻量思路，可直接迁移到去噪、去雨、去雾等多任务联合训练场景。
原文依据 decouples degradation adaptation from backbone computation through a retrieval-based degradation bank
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论一体化图像复原旨在用单一模型从多种异构退化中恢复干净图像，但在多种退化上以共享骨干网络联合训练往往会引入跨任务干扰与不稳定的优化过程，难以在所有任务上保持强劲性能。为解决这一问题，我们提出检索即复原（R2R），一个通过基于检索的退化库将退化适应与骨干网络计算解耦的轻量框架。具体而言，R2R 将退化知识外化为统一、退化专属的先验，并存储在紧凑的退化库中。训练阶段，退化聚合器在真值引导下聚合类内特征，形成任务级干净先验；推理阶段，退化匹配检索最相关的先验，用以调制骨干网络特征以完成复原。这种检索引导的设计显式地将退化线索与共享重建能力分离，从而实现稳定的多退化训练，并能直接扩展到更多退化类型。在包含一种、三种和五种退化的基准上的大量对比表明，R2R 在峰值信噪比上与最优的一体化方法相当，同时使用的乘加运算量减少约 91%。
英文原文摘要 1265 字符
All-in-one image restoration aims to recover clean images from heterogeneous degradations with a single model, but joint training on multiple degradations with a shared backbone often induces cross-task interference and unstable optimization, making it hard to maintain strong performance across all tasks. To address this, we propose Retrieve-to-Restore (R2R), a lightweight framework that decouples degradation adaptation from backbone computation through a retrieval-based degradation bank. Specifically, R2R externalizes degradation knowledge as unified, degradation-specific priors stored in a compact degradation bank. A degradation amalgamator aggregates intra-class features guided by ground truth into task-level clean priors during training, while degradation matching retrieves the most relevant prior at inference to modulate backbone features for restoration. This retrieval-guided design explicitly separates degradation cues from shared reconstruction capacity, enabling stable multi-degradation training and straightforward scaling to additional degradation types. Extensive comparisons on benchmarks with one, three, and five degradations show that R2R achieves PSNR on par with state-of-the-art all-in-one methods while using about 91% fewer MACs.