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pro vyhledávání: '"Panangian, Daniel"'
In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this proble
Externí odkaz:
http://arxiv.org/abs/2404.09277
Autor:
Panangian, Daniel, Bittner, Ksenia
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-resol
Externí odkaz:
http://arxiv.org/abs/2404.03930