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Transforming Thermal Images to Visible Spectrum Images using Deep Learning
Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Thermal spectrum cameras are gaining interest in many applications due to their long wavelength which allows them to operate under low light and harsh weather conditions. One disadvantage of thermal cameras is their limited visual interpretability for humans, which limits the scope of their applications. In this thesis, we try to address this problem by investigating the possibility of transforming thermal infrared (TIR) images to perceptually realistic visible spectrum (VIS) images by using Convolutional Neural Networks (CNNs). Existing state-of-the-art colorization CNNs fail to provide the desired output as they were trained to map grayscale VIS images to color VIS images. Instead, we utilize an auto-encoder architecture to perform cross-spectral transformation between TIR and VIS images. This architecture was shown to quantitatively perform very well on the problem while producing perceptually realistic images. We show that the quantitative differences are insignificant when training this architecture using different color spaces, while there exist clear qualitative differences depending on the choice of color space. Finally, we found that a CNN trained from day time examples generalizes well on tests from night time.

Place, publisher, year, edition, pages
2018. , p. 42
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-150617ISRN: LiTH-ISY-EX–18/5167–SEOAI: oai:DiVA.org:liu-150617DiVA, id: diva2:1242966
External cooperation
Swedish Defence Research Agency
Subject / course
Computer Vision Laboratory
Presentation
2018-08-27, Linköping, 15:00 (English)
Supervisors
Examiners
Available from: 2019-06-17 Created: 2018-08-29 Last updated: 2019-06-17Bibliographically approved

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Computer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
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  • apa
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