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Semantically Accurate Super-Resolution Generative Adversarial Networks. (arXiv:2205.08659v1 [cs.CV])
May 19, 2022, 1:10 a.m. | Tristan Frizza, Donald G. Dansereau, Nagita Mehr Seresht, Michael Bewley
cs.CV updates on arXiv.org arxiv.org
This work addresses the problems of semantic segmentation and image
super-resolution by jointly considering the performance of both in training a
Generative Adversarial Network (GAN). We propose a novel architecture and
domain-specific feature loss, allowing super-resolution to operate as a
pre-processing step to increase the performance of downstream computer vision
tasks, specifically semantic segmentation. We demonstrate this approach using
Nearmap's aerial imagery dataset which covers hundreds of urban areas at 5-7 cm
per pixel resolution. We show the proposed approach …
More from arxiv.org / cs.CV updates on arXiv.org
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