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Temporally Coherent GANs for Video Super-Resolution (TecoGAN) | Mengyu Chu
; You Xie
; Laura Leal-Taixé
; Nils Thuerey
; | Date: |
23 Nov 2018 | Abstract: | Adversarial training has been highly successful in the context of image
super-resolution. It was demonstrated to yield realistic and highly detailed
results. Despite this success, many state-of-the-art methods for video
super-resolution still favor simpler norms such as $L_2$ over adversarial loss
functions. This is caused by the fact that the averaging nature of direct
vector norms as loss functions leads to temporal smoothness. The lack of
spatial detail means temporal coherence is easily established. In our work, we
instead propose an adversarial training for video super-resolution that leads
to temporally coherent solutions without sacrificing spatial detail.
In our generator, we use a recurrent, residual framework that naturally
encourages temporal consistency. For adversarial training, we propose a novel
spatio-temporal discriminator in combination with motion compensation to
guarantee photo-realistic and temporally coherent details in the results. We
additionally identify a class of temporal artifacts in these recurrent
networks, and propose a novel Ping-Pong loss to remove them. Quantifying the
temporal coherence for image super-resolution tasks has also not been addressed
previously. We propose a first set of metrics to evaluate the accuracy as well
as the perceptual quality of the temporal evolution, and we demonstrate that
our method outperforms previous work by yielding realistic and detailed images
with natural temporal changes. | Source: | arXiv, 1811.9393 | Services: | Forum | Review | PDF | Favorites |
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