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).
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