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.

翻译：超分辨率（SR）模型正日益受到关注，用于在硬件受限条件下增强微创手术及诊断视频。然而，关于引入幻觉结构和放大噪声的有效担忧依然存在，这限制了其在安全关键场景中的可靠性。我们提出一种直接且实用的框架，通过识别重建可能失败的区域来提升超分辨率系统的可信度。该方法集成了一个轻量级的误差预测网络，该网络基于中间表示估计逐像素重建误差。该模块计算效率高、延迟低，适用于实时部署。通过构建保形故障掩码（CFM），我们将这些预测转化为操作层面的故障决策，从而定位超分辨率输出不可信的区域。基于保形风险控制原理，我们的方法提供了理论保证，能够同时控制容忍误差限度和检测故障中的未覆盖率。我们在图像和视频超分辨率任务上评估了该方法，证明其在内窥镜和机器人手术场景中检测不可靠重建的有效性。据我们所知，这是首个提供与模型无关、具有理论基础的框架以提升实时内窥镜图像超分辨率安全性的研究。