Abstract
Recent advances in volumetric super-resolution (SR) have demonstrated strong performance in medical and scientific imaging, with transformer- and CNN-based approaches achieving impressive results even at extreme scaling factors. In this work, we show that much of this performance stems from training on downsampled data rather than real low-resolution scans. This reliance on downsampling is partly driven by the scarcity of paired high- and low-resolution 3D datasets. To address this, 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: SR models trained on downsampled data produce substantially sharper predictions than those trained on real low-resolution scans, which smooth fine structures. Conversely, applying models trained on downsampled data 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 and 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 through: this https URL
Abstract (translated)
近期在体积超分辨率（SR）领域的研究进展已在医学与科学影像中展现出卓越性能，基于Transformer和CNN的方法甚至在极端缩放因子下仍能取得令人瞩目的成果。本研究指出，此类性能在很大程度上源于模型在降采样数据而非真实低分辨率扫描数据上的训练。这种对降采样的依赖部分源于成对高-低分辨率3D数据集的匮乏。为此，我们引入了VoDaSuRe——一个包含成对高-低分辨率扫描的大规模体积数据集。在该数据集上训练模型时，我们发现显著差异：在降采样数据上训练的SR模型能生成更锐利的预测结果，而在真实低分辨率扫描数据上训练的模型则会使精细结构平滑化。反之，将在降采样数据上训练的模型应用于真实扫描时，虽能保留更多结构但准确性不足。我们的研究表明，当前SR方法存在高估现象——当应用于真实数据时，它们无法恢复低分辨率扫描中丢失的结构，反而输出平滑化的平均预测。我们认为，基于深度学习的体积超分辨率研究需要像VoDaSuRe这样包含高复杂度真实成对扫描的数据集来推动进展。本数据集及代码已公开，可通过此链接获取。
URL
https://arxiv.org/abs/2603.23153
