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Abstract. Thermal imaging is a boon to the armed forces namely army, navy and airforce because of its day night working capability and ability to perform well in all weather conditions. Thermal detectors capture the infrared radiation emitted by all objects above absolute zero temperature. The temperature variations of the captured scene are represented as a thermogram. With the advent of infrared detector technology, the bulky cooled thermal detectors having moving parts and demanding cryogenic temperatures have transformed into small and less expensive uncooled microbolometers having no moving parts, thereby making systems more rugged requiring less maintenance. Thermal imaging due to its various advantages has a large number of applications in military and defence. It is popularly used by the army and navy for border surveillance and law enforcement. It is also used in ship collision avoidance and guidance systems. In the aviation industry it has greatly mitigated the risks of flying in low light and night conditions. They are widely used in military aviation to identify, locate and target the enemy forces. Recently, they are also being incorporated in civil aviation for health monitoring of aircrafts.

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This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by ×2, ×3 and ×4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the ×2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.