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Proceedings Paper

Systematic infrared image quality improvement using deep learning based techniques
Author(s): Huaizhong Zhang; Pablo Casaseca-de-la-Higuera; Chunbo Luo; Qi Wang; Matthew Kitchin; Andrew Parmley; Jesus Monge-Alvarez
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Paper Abstract

Infrared thermography (IRT, or thermal video) uses thermographic cameras to detect and record radiation in the longwavelength infrared range of the electromagnetic spectrum. It allows sensing environments beyond the visual perception limitations, and thus has been widely used in many civilian and military applications. Even though current thermal cameras are able to provide high resolution and bit-depth images, there are significant challenges to be addressed in specific applications such as poor contrast, low target signature resolution, etc. This paper addresses quality improvement in IRT images for object recognition. A systematic approach based on image bias correction and deep learning is proposed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. Our main objective is to maximise the useful information on the object to be detected even when the number of pixels on target is adversely small. The experimental results show that our approach can significantly improve target resolution and thus helps making object recognition more efficient in automatic target detection/recognition systems (ATD/R).

Paper Details

Date Published: 26 October 2016
PDF: 8 pages
Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080P (26 October 2016); doi: 10.1117/12.2242036
Show Author Affiliations
Huaizhong Zhang, Univ. of the West of Scotland (United Kingdom)
Pablo Casaseca-de-la-Higuera, Univ. of the West of Scotland (United Kingdom)
Chunbo Luo, Univ. of Exeter (United Kingdom)
Qi Wang, Univ. of the West of Scotland (United Kingdom)
Matthew Kitchin, Thales UK (United Kingdom)
Andrew Parmley, Thales UK (United Kingdom)
Jesus Monge-Alvarez, Univ. of the West of Scotland (United Kingdom)

Published in SPIE Proceedings Vol. 10008:
Remote Sensing Technologies and Applications in Urban Environments
Thilo Erbertseder; Thomas Esch; Nektarios Chrysoulakis, Editor(s)

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