# EndoL2H: Deep Super-Resolution for Capsule Endoscopy

arXiv: https://arxiv.org/abs/2002.05459 · HTML: https://mlsanity.com/paper/2002.05459/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Towards Automatic Recognition of Pure & Mixed Stones using Intraoperative Endoscopic Digital Images](https://mlsanity.com/paper/2105.10686/) (2021)
- [Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models](https://mlsanity.com/paper/2209.14467/) (2022)
- [Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution](https://mlsanity.com/paper/2406.18310/) (2024)
- [MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation](https://mlsanity.com/paper/2509.21265/) (2025)
- [Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2604.21814/) (2026)
- [SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures](https://mlsanity.com/paper/2507.00209/) (2025)
- [Can Adversarial Networks Make Uninformative Colonoscopy Video Frames Clinically Informative?](https://mlsanity.com/paper/2304.02152/) (2023)
- [Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization](https://mlsanity.com/paper/2309.09392/) (2023)
- [Uncertainty-Aware Regularization for Image-to-Image Translation](https://mlsanity.com/paper/2412.01705/) (2024)
- [Colon-X: Advancing Intelligent Colonoscopy toward Clinical Reasoning](https://mlsanity.com/paper/2512.03667/) (2025)
- [DGGAN: Degradation Guided Generative Adversarial Network for Real-time Endoscopic Video Enhancement](https://mlsanity.com/paper/2512.07253/) (2025)
- [UnCapsTSR: An Unsupervised Transformer-based Image Super-Resolution Approach for Capsule Endoscopy Images](https://mlsanity.com/paper/2609.02476/) (2026)
- [CEM-TUDASR: Computationally efficient multi-modality transformer based unsupervised domain adaptive super-resolution approach](https://mlsanity.com/paper/2609.11201/) (2026)

## Abstract (arXiv)

Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics. Enhanced-resolution endoscopy has shown to improve adenoma detection rate for conventional endoscopy and is likely to do the same for capsule endoscopy. In this work, we propose and quantitatively validate a novel framework to learn a mapping from low-to-high resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the resolution by up to factors of 8x, 10x, 12x, respectively. Quantitative and qualitative studies performed demonstrate the superiority of EndoL2H over state-of-the-art deep super-resolution methods DBPN, RCAN and SRGAN. MOS tests performed by 30 gastroenterologists qualitatively assess and confirm the clinical relevance of the approach. EndoL2H is generally applicable to any endoscopic capsule system and has the potential to improve diagnosis and better harness computational approaches for polyp detection and characterization. Our code and trained models are available at https://github.com/CapsuleEndoscope/EndoL2H.