@InProceedings{RafaelE.Rivadeneira2022,
author="Rafael E. Rivadeneira
and Angel Sappa
and Boris X. Vintimilla",
title="Multi-Image Super-Resolution for Thermal Images",
booktitle="17th International Conference on Computer Vision Theory and Applications (VISAPP 2022)",
year="2022",
volume="4",
pages="635--642",
optkeywords="Thermal Images",
optkeywords="Multi-view",
optkeywords="Multi-frame",
optkeywords="Super-Resolution",
optkeywords="Deep Learning",
optkeywords="Attention Block",
abstract="This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses twoattention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches.",
optnote="MSIAU; 601.349",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3690), last updated on Thu, 27 Apr 2023 15:07:39 +0200",
opturl="https://www.scitepress.org/PublicationsDetail.aspx?ID=gGSl9NFalmw=&t=1",
file=":http://158.109.8.37/files/RSV2022a.pdf:PDF"
}