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Original scientific paper
https://doi.org/10.2498/cit.1001813

Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?

Katherine Hanton ; University of South Australia
Jadranka Sunde ; Defence Science and Technology Organisation
Marcus Butavicius ; University of Adelaide
Nicholas Burns ; University of Adelaide

Fulltext: english, pdf (2 MB) pages 141-150 downloads: 1.031* cite
APA 6th Edition
Hanton, K., Sunde, J., Butavicius, M. & Burns, N. (2010). Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?. Journal of computing and information technology, 18 (2), 141-150. https://doi.org/10.2498/cit.1001813
MLA 8th Edition
Hanton, Katherine, et al. "Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?." Journal of computing and information technology, vol. 18, no. 2, 2010, pp. 141-150. https://doi.org/10.2498/cit.1001813. Accessed 20 Jul. 2019.
Chicago 17th Edition
Hanton, Katherine, Jadranka Sunde, Marcus Butavicius and Nicholas Burns. "Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?." Journal of computing and information technology 18, no. 2 (2010): 141-150. https://doi.org/10.2498/cit.1001813
Harvard
Hanton, K., et al. (2010). 'Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?', Journal of computing and information technology, 18(2), pp. 141-150. https://doi.org/10.2498/cit.1001813
Vancouver
Hanton K, Sunde J, Butavicius M, Burns N. Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?. Journal of computing and information technology [Internet]. 2010 [cited 2019 July 20];18(2):141-150. https://doi.org/10.2498/cit.1001813
IEEE
K. Hanton, J. Sunde, M. Butavicius and N. Burns, "Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?", Journal of computing and information technology, vol.18, no. 2, pp. 141-150, 2010. [Online]. https://doi.org/10.2498/cit.1001813

Abstracts
The ability to detect dangerous objects (such as improvised explosive devices) from a distance is important in security and military environments. Standoff imaging can produce images that have been degraded by atmospheric turbulence, movement, blurring and other factors. The number and size of pixels in the imaging sensor can also contribute to image degradation through under-sampling of the image. Establishing processes that enhance degraded or under-sampled infrared images so that objects of interest can be recognised with more certainty is important. In this paper, super-resolution image reconstruction and deconvolution methods are explored, with an emphasis on quantifying and understanding human operator detection performance.

Keywords
standoff detection; infrared imaging; super-resolution; performance improvement measure

Hrčak ID: 59526

URI
https://hrcak.srce.hr/59526

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