TY - GEN
T1 - Gan-Based Video Super-Resolution with Direct Regularized Inversion of the Low-Resolution Formation Model
AU - Lopez-Tapia, Santiago
AU - Lucas, Alice
AU - Molina, Rafael
AU - Katsaggelos, Aggelos K.
N1 - Funding Information:
This work was supported in part by the Sony 2016 Research Award Program Research Project. The work of SLT and RM was supported by the the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869-C2-2-R and the Visiting Scholar program at the University of Granada. SLT received financial support through the Spanish FPU program.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Convolutional Neuronal Networks
KW - Generative Adversarial Networks
KW - Perceptual Loss Functions
KW - Super-resolution
KW - Video
UR - http://www.scopus.com/inward/record.url?scp=85076817145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076817145&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2019.8803709
DO - 10.1109/ICIP.2019.8803709
M3 - Conference contribution
AN - SCOPUS:85076817145
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2886
EP - 2890
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - IEEE Computer Society
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
Y2 - 22 September 2019 through 25 September 2019
ER -