用智能代理提升内镜图像质量，精准分割息肉。
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent
- 基于CLIP语义分析的图像质量评估与动态增强策略
- 通过反馈机制优化像素级增强与分割效果
- 模块化设计适合嵌入式内镜设备部署
由于人因与环境因素干扰，拍摄的息肉图像常存在光照不足、模糊、过曝等问题，给下游分割任务带来挑战。为应对噪声导致的图像退化问题，我们提出AgentPolyp框架，结合基于CLIP的语义引导与轻量神经网络，实现动态多模态图像增强（如去噪、对比度调整）和分割一体化。该智能体首先利用CLIP驱动的语义分析评估图像质量（例如识别“低对比度且具血管纹理的息肉”），并自适应地应用强化学习策略选择增强操作；通过质量评估反馈环路协同优化像素级增强与分割关注区域，确保在神经网络分割前完成鲁棒预处理。该模块化架构支持多种增强算法与分割网络的即插即用扩展，满足内窥镜设备的部署需求。
原文摘要 · Abstract (English)
Since human and environmental factors interfere, captured polyp images usually suffer from issues such as dim lighting, blur, and overexposure, which pose challenges for downstream polyp segmentation tasks. To address the challenges of noise-induced degradation in polyp images, we present AgentPolyp, a novel framework integrating CLIP-based semantic guidance and dynamic image enhancement with a lightweight neural network for segmentation. The agent first evaluates image quality using CLIP-driven semantic analysis (e.g., identifying ``low-contrast polyps with vascular textures") and adapts reinforcement learning strategies to dynamically apply multi-modal enhancement operations (e.g., denoising, contrast adjustment). A quality assessment feedback loop optimizes pixel-level enhancement and segmentation focus in a collaborative manner, ensuring robust preprocessing before neural network segmentation. This modular architecture supports plug-and-play extensions for various enhancement algorithms and segmentation networks, meeting deployment requirements for endoscopic devices.
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