Spatially Adaptive Losses for Video Super-resolution with GANs
Xijun Wang, Alice Lucas, Santiago Lopez Tapia, Xinyi Wu, Rafael Molina, Aggelos K. Katsaggelos
Abstract
Deep Learning techniques and more specifically Generative Adversarial Networks (GANs) have recently been used for solving the video super-resolution (VSR) problem. In some of the published works, feature-based perceptual losses have also been used, resulting in promising results. While there has been work in the literature incorporating temporal information into the loss function, studies which make use of the spatial activity to improve GAN models are still lacking. Towards this end, this paper aims to train a GAN guided by a spatially adaptive loss function. Experimental results demonstrate that the learned model achieves improved results with sharper images, fewer artifacts and less noise.
BibTeX
@inproceedings{icassp2019_spatiallyadaptiv,
title = {Spatially Adaptive Losses for Video Super-resolution with GANs},
author = {Xijun Wang and Alice Lucas and Santiago Lopez Tapia and Xinyi Wu and Rafael Molina and Aggelos K. Katsaggelos},
booktitle = {ICASSP 2019},
year = {2019}
}