EndoIR：通过噪声感知路由扩散实现与退化无关的一体化内窥镜图像恢复
EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion
AI总结：
针对内窥镜图像多种退化共存且现有方法需先验知识的问题，提出一体化退化无关扩散框架EndoIR，通过双域提示器、自适应嵌入和噪声感知路由块，在少参数下实现多退化场景SOTA恢复性能并验证临床实用价值。
摘要：
内窥镜图像常受低光照、烟雾、出血等多样且共存的退化影响，掩盖关键临床细节。现有恢复方法通常针对特定任务，且往往需要退化类型的先验知识，限制了其在真实临床应用中的鲁棒性。我们提出EndoIR，一种一体化、与退化无关的基于扩散的框架，可通过单一模型恢复多种退化类型。EndoIR引入双域提示器，提取联合空间-频率特征，并结合自适应嵌入，将共享和任务特定线索编码为去噪条件。为缓解传统基于拼接的条件中的特征混淆，我们设计双流扩散架构，分别处理清洁和退化输入，通过校正融合块以结构化、退化感知的方式整合它们。此外，噪声感知路由块通过在去噪过程中动态选择仅与噪声相关的特征来提高效率。在SegSTRONG-C和CEC数据集上的实验表明，EndoIR在多种退化场景下实现了最先进的性能，同时使用比强基线更少的参数，下游分割实验证实了其临床实用性。
英文摘要：
Endoscopic images often suffer from diverse and co-occurring degradations such as low lighting, smoke, and bleeding, which obscure critical clinical details. Existing restoration methods are typically task-specific and often require prior knowledge of the degradation type, limiting their robustness in real-world clinical use. We propose EndoIR, an all-in-one, degradation-agnostic diffusion-based framework that restores multiple degradation types using a single model. EndoIR introduces a Dual-Domain Prompter that extracts joint spatial-frequency features, coupled with an adaptive embedding that encodes both shared and task-specific cues as conditioning for denoising. To mitigate feature confusion in conventional concatenation-based conditioning, we design a Dual-Stream Diffusion architecture that processes clean and degraded inputs separately, with a Rectified Fusion Block integrating them in a structured, degradation-aware manner. Furthermore, Noise-Aware Routing Block improves efficiency by dynamically selecting only noise-relevant features during denoising. Experiments on SegSTRONG-C and CEC datasets demonstrate that EndoIR achieves state-of-the-art performance across multiple degradation scenarios while using fewer parameters than strong baselines, and downstream segmentation experiments confirm its clinical utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
