Thermal Image Super-resolution: A Novel Architecture and Dataset

Rafael Rivadeneira, Angel Sappa, Boris Vintimilla

Abstract

This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in a rig trying to minimize the baseline distance to make easier the registration problem. The proposed architecture is based on ResNet6 as a Generator and PatchGAN as a Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available.

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Paper Citation


in Harvard Style

Rivadeneira R., Sappa A. and Vintimilla B. (2020). Thermal Image Super-resolution: A Novel Architecture and Dataset.In - VISAPP, ISBN , pages 0-0. DOI: 10.5220/0009173601110119


in Bibtex Style

@conference{visapp20,
author={Rafael Rivadeneira and Angel Sappa and Boris Vintimilla},
title={Thermal Image Super-resolution: A Novel Architecture and Dataset},
booktitle={ - VISAPP,},
year={2020},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009173601110119},
isbn={},
}


in EndNote Style

TY - CONF

JO - - VISAPP,
TI - Thermal Image Super-resolution: A Novel Architecture and Dataset
SN -
AU - Rivadeneira R.
AU - Sappa A.
AU - Vintimilla B.
PY - 2020
SP - 0
EP - 0
DO - 10.5220/0009173601110119