Trustworthy Endoscopic Super-Resolution
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 de...
| Authors | Julio Silva-Rodríguez, Ender Konukoglu |
|---|---|
| Primary category | cs.CV |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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