Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network

arXiv:2404.03930v1 Announce Type: cross
Abstract: A low-resolution digital surface model (DSM) features distinctive attributes impacted by noise, sensor limitations and data acquisition conditions, which failed to be replicated using simple interpolation methods like bicubic. This causes super-resolution models trained on synthetic data does not perform effectively on real ones. Training a model on real low and high resolution DSMs pairs is also a challenge because of the lack of information. On the other hand, the existence of other imaging modalities …

abstract acquisition arxiv cs.cv data digital edge eess.iv features limitations low network noise residual resolution sensor simple surface synthetic synthetic data training type via world