VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution

Recent advances in volumetric super-resolution (SR) have demonstrated great performance in medical and scientific imaging, with transformer- and CNN-based approaches achieving impressive results even at extreme scaling factors. We show that this impressive performance largely stems from training on downsampled data rather than real low-resolution scans. Such a training setup arises partly from the limited availability of paired high- and low-resolution volumetric datasets. To address this gap, we introduce VoDaSuRe, a large-scale volumetric dataset containing paired high- and low-resolution scans. When training models on VoDaSuRe, we reveal a significant discrepancy: models trained on downscaled data produce substantially sharper predictions than those trained on real low-resolution scans, which smooth fine structures. Conversely, applying downscaled trained models to real scans preserves more structure but is inaccurate. Our findings suggest that current SR methods are overstated - when applied to real data, they do not recover structures lost in low-resolution scans but instead predict a smoothed average. We argue that progress in deep learning-based volumetric SR requires datasets with paired real scans of high complexity, such as VoDaSuRe. Our dataset and code are publicly available at linkwhenpublished.