Trustworthy Endoscopic Super-Resolution
For safety-critical endoscopic surgery, this work provides a theoretically grounded, model-agnostic method to improve trustworthiness of SR, addressing a key reliability concern.
The paper proposes a framework to make super-resolution models more trustworthy in endoscopic surgery by predicting where reconstructions are likely to fail, using a lightweight error-prediction network and Conformal Failure Masks with theoretical guarantees. The method is evaluated on image and video SR, showing effectiveness in detecting unreliable reconstructions.
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.