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Trustworthy Endoscopic Super-Resolution

arXiv 2604.18001 ↗First posted 20 Apr 2026cs.CVcited by 0

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Abstract (arXiv)

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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References · 34

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
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  6. 2019Conformalized Quantile Regressioncited by 629
  7. 2019Conformal Prediction Under Covariate Shiftcited by 519
  8. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 366
  9. 2022Conformal prediction beyond exchangeabilitycited by 355
  10. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 342
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  12. 2022Conformal Risk Controlcited by 299
22 more
  1. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 272
  2. 2020Single Image Super-Resolution via a Holistic Attention Networkcited by 265
  3. 2019Addressing Failure Prediction by Learning Model Confidencecited by 186
  4. 2013Learning by Transductioncited by 121
  5. 2023Class-Conditional Conformal Prediction with Many Classescited by 109
  6. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  7. 2021Fair Conformal Predictors for Applications in Medical Imagingcited by 74
  8. 2021DOCTOR: A Simple Method for Detecting Misclassification Errorscited by 53
  9. 2023How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Controlcited by 50
  10. 2022Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Setscited by 38
  11. 2020Confidence Estimation via Auxiliary Modelscited by 34
  12. 2023Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithmscited by 14
  13. 2025Conformal Prediction for Image Segmentation Using Morphological Prediction Setscited by 14
  14. 2025Full Conformal Adaptation of Medical Vision-Language Modelscited by 9
  15. 2025ViLU: Learning Vision-Language Uncertainties for Failure Predictioncited by 9
  16. 2025Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomographycited by 8
  17. 2025Trustworthy Few-Shot Transfer of Medical VLMs through Split Conformal Predictioncited by 8
  18. 2025Pitfalls of Conformal Predictions for Medical Image Classificationcited by 6
  19. 2024Robust Conformal Volume Estimation in 3D Medical Imagescited by 4
  20. 2025Conformal forecasting for surgical instrument trajectorycited by 3
  21. 2025MedVSR: Medical Video Super-Resolution with Cross State-Space Propagationcited by 3
  22. 2025SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedurescited by 2

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