@inproceedings{Santiago2019a,
abstract = {While high and ultra high definition displays are becoming popular, most of the available content has been acquired at much lower resolutions. In this work we propose to pseudo-invert with regularization the image formation model using GANs and perceptual losses. Our model, which does not require the use of motion compensation, utilizes explicitly the low resolution image formation model and additionally introduces two feature losses which are used to obtain perceptually improved high resolution images. The experimental validation shows that our approach outperforms current video super resolution learning based models.},
author = {Lopez-Tapia, Santiago and Lucas, Alice and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2019 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2019.8803709},
isbn = {978-1-5386-6249-6},
issn = {15224880},
keywords = {Convolutional Neuronal Networks,Generative Adversarial Networks,Perceptual Loss Functions,Super-resolution,Video},
month = {sep},
pages = {2886--2890},
publisher = {IEEE},
title = {{Gan-Based Video Super-Resolution With Direct Regularized Inversion of the Low-Resolution Formation Model}},
url = {https://ieeexplore.ieee.org/document/8803709/},
volume = {2019-Septe},
year = {2019}
}

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