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).
Huaizhong Zhang, Pablo Casaseca-de-la-Higuera, Chunbo Luo, Qi Wang, Matthew Kitchin, Andrew Parmley, and Jesus Monge-Alvarez, "Systematic infrared image quality improvement using deep learning based techniques," Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080P (Presented at SPIE Remote Sensing: September 27, 2016; Published: 26 October 2016); https://doi.org/10.1117/12.2242036.
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