发表机构
University of Electronic Science and Technology of China; Jiigan Technology（电子科技大学; 极感科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对一体化图像恢复中退化与内容线索纠缠的双歧义问题，提出DAR-Net网络，经实验在多退化设置下较基准方法实现峰值信噪比提升，在多基准数据集表现优异。
AI 中文摘要
一体化图像恢复旨在在统一框架内处理多种退化类型。现有方法通常在共享潜在空间中编码异质退化条件，导致退化相关线索与场景内容相互纠缠。我们将该挑战定义为双歧义：通道调制中的语义歧义，以及恢复响应中的空间歧义，这会导致内容损坏和残留伪影。为缓解此问题，我们提出DAR-Net，即面向一体化图像恢复的双歧义校正网络。DAR-Net首先引入退化原型表示（Degradation Archetype Representation，DAR）模块，通过单纯形约束的原型混合建模构建结构化退化状态。基于该状态，语义歧义校正（Semantic Ambiguity Rectification，SeAR）模块生成感知退化的提示，以改进解码器中的通道条件设置。空间歧义校正（Spatial Ambiguity Rectification，SpAR）模块进一步将感知退化的互补特征正则化为正交响应子空间，减少移除与保留线索间的空间干扰。在标准一体化恢复基准上的大量实验表明，DAR-Net在3种退化和5种退化设置下均实现最佳整体性能，平均峰值信噪比（PSNR）较最强竞争者分别提升0.14 dB和0.34 dB；其在CDD-11和WeatherBench上也表现出优异性能。
英文摘要
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.
