Qualitative and quantitative comparisons of multispectral night vision colorization techniques

Abstract

Multispectral images enable robust night vision (NV) object assessment over day-night conditions. Furthermore, colorized multispec- tral NV images can enhance human vision by improving observer object classification and reaction times, especially for low light conditions. NV colorization techniques can produce the colorized images that closely resemble natural scenes. Qualitative (subjective) and quantitative (objec- tive) comparisons of NV colorization techniques proposed in the past decade are made and two categories of coloring methods, color fusion and color mapping, are discussed and compared. Color fusion directly combines multispectral NV images into a color-version image by mixing pixel intensities at different color planes, of which a channel-based color fusion method is reviewed. Color-mapping usually maps the color properties of a false-colored NV image (source) onto that of a true- color daylight target picture (reference). Four coloring-mapping meth- ods—statistical matching, histogram matching, joint histogram matching, and look-up table (LUT)—are presented and compared, including a new color-mapping method called joint-histogram matching (JHM). The experi- mental NV imagery includes visible (Red-Green-Blue), image-intensified, near infrared, and long-wave infrared images. The qualitative evaluations are conducted by visual inspections of the colorized images, whereas the quantitative evaluations are achieved by a newly proposed metric, objec- tive evaluation index. From the experimental results according to both qualitative and quantitative evaluations, the following conclusions can be drawn: the segmentation-based colorization method produces very impressive and realistic colors but requires intense computations; color fusion and LUT-based methods run very fast but with less realistic results; the statistic-matching method always provides acceptable results; histo- gram matching and joint-histogram matching can generate impressive and vivid colors when the color distributions between source and target are similar; and the statistic-matching then joint-histogram matching (SM-JHM) method is a reliable and efficient method recommended from both qualitative and quantitative evaluations.