为内窥镜超分提供可信度评估，识别不可信区域
Trustworthy Endoscopic Super-Resolution
- 用轻量误差预测网络分析中间特征，判断重建失败位置
- 生成符合风险控制理论的故障掩码，误检率可保障
- 适合对安全性要求高的手术视频实时增强场景
超分辨率（SR）模型在硬件受限条件下提升微创手术与诊断视频质量受到广泛关注。但其可能引入幻觉结构并放大噪声，影响安全关键场景下的可靠性。本文提出一种直接且实用的框架，通过识别重建易出错区域来提升SR系统的可信度。方法整合轻量级误差预测网络，基于中间表示进行像素级重建误差估计，计算高效、延迟低，适合实时部署。将预测结果转化为操作性故障决策，构建符合同分布风险控制原理的置信失败掩码（CFM），可理论保障误差容忍上限与故障漏检率。在图像与视频超分任务上验证，有效检测内窥镜与机器人手术场景中的不可靠重建区域。据我们所知，这是首个模型无关、理论严谨的实时内窥镜图像超分安全保障研究。
原文摘要 · Abstract (English)
Super-resolution (SR) models are attracting growing interest for enhancing minimally invasive surgery and diagnostic videos under hardware constraints. However, valid concerns remain regarding the introduction of hallucinated structures and amplified noise, limiting their reliability in safety-critical settings. We propose a direct and practical framework to make SR systems more trustworthy by identifying where reconstructions are likely to fail. Our approach integrates a lightweight error-prediction network that operates on intermediate representations to estimate pixel-wise reconstruction error. The module is computationally efficient and low-latency, making it suitable for real-time deployment. We convert these predictions into operational failure decisions by constructing Conformal Failure Masks (CFM), which localize regions where the SR output should not be trusted. Built on conformal risk control principles, our method provides theoretical guarantees for controlling both the tolerated error limit and the miscoverage in detected failures. We evaluate our approach on image and video SR, demonstrating its effectiveness in detecting unreliable reconstructions in endoscopic and robotic surgery settings. To our knowledge, this is the first study to provide a model-agnostic, theoretically grounded approach to improving the safety of real-time endoscopic image SR.
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