PT Unknown
AU Rafael E. Rivadeneira
   Angel Sappa
   Boris X. Vintimilla
TI Thermal Image Super-Resolution: A Novel Unsupervised Approach
BT International Joint Conference on Computer Vision, Imaging and Computer Graphics
PY 2022
BP 495–506
VL 1474
AB This paper proposes the use of a CycleGAN architecture for thermal image super-resolution under a transfer domain strategy, where middle-resolution images from one camera are transferred to a higher resolution domain of another camera. The proposed approach is trained with a large dataset acquired using three thermal cameras at different resolutions. An unsupervised learning process is followed to train the architecture. Additional loss function is proposed trying to improve results from the state of the art approaches. Following the first thermal image super-resolution challenge (PBVS-CVPR2020) evaluations are performed. A comparison with previous works is presented showing the proposed approach reaches the best results.
ER