Commit cb71a367 authored by Matej Choma's avatar Matej Choma

add report

parent 01900398
This diff is collapsed.
......@@ -3,13 +3,111 @@
% pokud nechcem aby nam to zmensovalo pismenka v nazvech titulu,
% tak to obklopit chlupatejma zavorkama
@misc{ zizka,
title = {Some interesting source},
author = { Jan {\v Z}i{\v z}ka},
year = {1415},
howpublished = {online},
note = {[cit.~2018--10--11] \url{http://zizka.trocnov/husiti/conspiration.pdf} }
@InProceedings{DIV2K,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
@inproceedings{cai2019ntire,
title={Ntire 2019 challenge on real image super-resolution: Methods and results},
author={Cai, Jianrui and Gu, Shuhang and Timofte, Radu and Zhang, Lei},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
pages={0--0},
year={2019}
}
@article{UnetCascade,
author = {Ruicheng Feng and
Jinjin Gu and
Yu Qiao and
Chao Dong},
title = {Suppressing Model Overfitting for Image Super-Resolution Networks},
journal = {CoRR},
volume = {abs/1906.04809},
year = {2019},
url = {http://arxiv.org/abs/1906.04809},
archivePrefix = {arXiv},
eprint = {1906.04809},
timestamp = {Mon, 30 Nov 2020 16:24:54 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-04809.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{RCAB,
title={Image super-resolution using very deep residual channel attention networks},
author={Zhang, Yulun and Li, Kunpeng and Li, Kai and Wang, Lichen and Zhong, Bineng and Fu, Yun},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={286--301},
year={2018}
}
@article{SRGAN,
author = {Christian Ledig and
Lucas Theis and
Ferenc Huszar and
Jose Caballero and
Andrew P. Aitken and
Alykhan Tejani and
Johannes Totz and
Zehan Wang and
Wenzhe Shi},
title = {Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial
Network},
journal = {CoRR},
volume = {abs/1609.04802},
year = {2016},
url = {http://arxiv.org/abs/1609.04802},
archivePrefix = {arXiv},
eprint = {1609.04802},
timestamp = {Mon, 13 Aug 2018 16:48:38 +0200},
biburl = {https://dblp.org/rec/journals/corr/LedigTHCATTWS16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{gans,
title={Generative adversarial nets},
author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
journal={Advances in neural information processing systems},
volume={27},
pages={2672--2680},
year={2014}
}
@inproceedings{Set14,
title={On single image scale-up using sparse-representations},
author={Zeyde, Roman and Elad, Michael and Protter, Matan},
booktitle={International conference on curves and surfaces},
pages={711--730},
year={2010},
organization={Springer}
}
@inproceedings{shi2016real,
title={Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network},
author={Shi, Wenzhe and Caballero, Jose and Husz{\'a}r, Ferenc and Totz, Johannes and Aitken, Andrew P and Bishop, Rob and Rueckert, Daniel and Wang, Zehan},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={1874--1883},
year={2016}
}
@article{farsiu2004fast,
title={Fast and robust multiframe super resolution},
author={Farsiu, Sina and Robinson, M Dirk and Elad, Michael and Milanfar, Peyman},
journal={IEEE transactions on image processing},
volume={13},
number={10},
pages={1327--1344},
year={2004},
publisher={IEEE}
}
@inproceedings{sajjadi2018frame,
title={Frame-recurrent video super-resolution},
author={Sajjadi, Mehdi SM and Vemulapalli, Raviteja and Brown, Matthew},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={6626--6634},
year={2018}
}
......@@ -18,14 +18,47 @@
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\citation{zizka}
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\begin{thebibliography}{1}
\bibitem{DIV2K}
Eirikur Agustsson and Radu Timofte.
\newblock Ntire 2017 challenge on single image super-resolution: Dataset and
study.
\newblock In {\em The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) Workshops}, July 2017.
\bibitem{cai2019ntire}
Jianrui Cai, Shuhang Gu, Radu Timofte, and Lei Zhang.
\newblock Ntire 2019 challenge on real image super-resolution: Methods and
results.
\newblock In {\em Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition Workshops}, pages 0--0, 2019.
\bibitem{farsiu2004fast}
Sina Farsiu, M~Dirk Robinson, Michael Elad, and Peyman Milanfar.
\newblock Fast and robust multiframe super resolution.
\newblock {\em IEEE transactions on image processing}, 13(10):1327--1344, 2004.
\bibitem{UnetCascade}
Ruicheng Feng, Jinjin Gu, Yu~Qiao, and Chao Dong.
\newblock Suppressing model overfitting for image super-resolution networks.
\newblock {\em CoRR}, abs/1906.04809, 2019.
\bibitem{gans}
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair, Aaron Courville, and Yoshua Bengio.
\newblock Generative adversarial nets.
\newblock {\em Advances in neural information processing systems},
27:2672--2680, 2014.
\bibitem{SRGAN}
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew~P. Aitken,
Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi.
\newblock Photo-realistic single image super-resolution using a generative
adversarial network.
\newblock {\em CoRR}, abs/1609.04802, 2016.
\bibitem{sajjadi2018frame}
Mehdi~SM Sajjadi, Raviteja Vemulapalli, and Matthew Brown.
\newblock Frame-recurrent video super-resolution.
\newblock In {\em Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition}, pages 6626--6634, 2018.
\bibitem{shi2016real}
Wenzhe Shi, Jose Caballero, Ferenc Husz{\'a}r, Johannes Totz, Andrew~P Aitken,
Rob Bishop, Daniel Rueckert, and Zehan Wang.
\newblock Real-time single image and video super-resolution using an efficient
sub-pixel convolutional neural network.
\newblock In {\em Proceedings of the IEEE conference on computer vision and
pattern recognition}, pages 1874--1883, 2016.
\bibitem{RCAB}
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu.
\newblock Image super-resolution using very deep residual channel attention
networks.
\newblock In {\em Proceedings of the European Conference on Computer Vision
(ECCV)}, pages 286--301, 2018.
\end{thebibliography}
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\T1/lmr/m/n/10.95 (-20) Video res-o-lu-tion up-scal-ing, also called super-reso
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[]\T1/lmr/m/n/10.95 (-20) pre-trained on the DIV2K dataset for 10 epochs
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[]\T1/lmr/m/n/10.95 (-20) While U-Net ben-e-fits from the DIV2K pre-training,
[]
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