Abstract
Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a super-resolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process, a new thermal images dataset is used. Different experiments have been carried out, firstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset.
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Acknowledgment
This work has been partially supported by: the ESPOL project PRAIM (FIEC-09-2015); the Spanish Government under Project TIN2017-89723-P; and the “CERCA Programme/Generalitat de Catalunya”. The authors thanks CTI-ESPOL for sharing server infrastructure used for training and testing the proposed work. The authors gratefully acknowledge the support of the CYTED Network: “Ibero-American Thematic Network on ICT Applications for Smart Cities” (REF-518RT0559) and the NVIDIA Corporation for the donation of the Titan Xp GPU used for this research.
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Rivadeneira, R.E., Suárez, P.L., Sappa, A.D., Vintimilla, B.X. (2019). Thermal Image SuperResolution Through Deep Convolutional Neural Network. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_37
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