Trustworthy Endoscopic Super-Resolution

TL;DR

This paper introduces a real-time, trustworthy super-resolution framework for endoscopic videos that predicts reconstruction errors and localizes unreliable regions, enhancing safety in medical imaging.

Contribution

It presents a lightweight, error-prediction module combined with conformal failure masks, providing theoretical guarantees for detecting unreliable super-resolution outputs in real-time.

Findings

Effective detection of unreliable reconstructions in endoscopic videos.

The method offers theoretical guarantees for error control.

Demonstrated success in medical imaging and surgical settings.

Abstract

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.