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Proceedings Paper

Qualitative evaluations and comparisons of six night-vision colorization methods
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Paper Abstract

Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test (“Ishihara Color Blindness Test”) and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF < SM < SM-JHM < LUT < JHM < HM. It is anticipated that this work will provide a benchmark for NV colorization and for quantitative evaluation using an objective metric such as objective evaluation index (OEI).

Paper Details

Date Published: 23 May 2013
PDF: 11 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 874511 (23 May 2013); doi: 10.1117/12.2016643
Show Author Affiliations
Yufeng Zheng, Alcorn State Univ. (United States)
Kristopher Reese, Univ. of Louisville (United States)
Erik Blasch, Air Force Research Lab. (United States)
Paul McManamon, Exciting Technology LLC (United States)

Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)

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