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Unpaired Image Super-Resolution using Pseudo-Supervision
[article]
2020
arXiv
pre-print
In most studies on learning-based image super-resolution (SR), the paired training dataset is created by downscaling high-resolution (HR) images with a predetermined operation (e.g., bicubic). However, these methods fail to super-resolve real-world low-resolution (LR) images, for which the degradation process is much more complicated and unknown. In this paper, we propose an unpaired SR method using a generative adversarial network that does not require a paired/aligned training dataset. Our
arXiv:2002.11397v1
fatcat:xqrg7vlrzzfhheanqwmnhzuwl4