这篇论文做了个 DAR-Net，把不同图像退化分开处理，三个和五个退化任务上 PSNR 都超过现有方法，做图像恢复的可以看看。
DAR-Net 在统一框架中处理去雨、去雾、去模糊等退化，提出退化原型表示模块构造结构化退化状态。语义歧义校正模块生成退化感知提示，空间歧义校正模块将特征约束到正交响应子空间。在三个退化和五个退化设置下，平均 PSNR 比最强对手分别提高 0.14 dB 和 0.34 dB，在 CDD-11 和 WeatherBench 上也表现更好。
What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration
All-in-one image restoration aims to handle diverse degradations within a unified framework. Existing methods commonly encode heterogeneous degradation conditions in a shared latent space, where degradation-related cues and scene content can remain entangled. We characterize the resulting challenge as dual ambiguity: semantic ambiguity in channel-wise modulation and spatial ambiguity in restoration responses, which can lead to content corruption and residual artifacts. To mitigate this issue, we propose DAR-Net, a Dual-Ambiguity Rectification Network for all-in-one image restoration. DAR-Net first introduces a Degradation Archetype Representation (DAR) module to construct a structured degradation state through simplex-constrained archetype mixture modeling. Based on this state, a Semantic Ambiguity Rectification (SeAR) module generates degradation-aware prompts to improve channel-wise conditioning in the decoder. A Spatial Ambiguity Rectification (SpAR) module further regularizes degradation-aware and complementary features toward orthogonal response subspaces, reducing spatial interference between removal and preservation cues. Extensive experiments on standard all-in-one restoration benchmarks show that DAR-Net achieves the best overall performance under both three-degradation and five-degradation settings, improving the average PSNR over the strongest competitor by 0.14 dB and 0.34 dB, respectively; it additionally shows superior performance on CDD-11 and WeatherBench.