Objective quality evaluation of visible and infrared color fusion image

Yuan, Yunhao; Zhang, Junju; Chang, Benkang; Han, Yu

doi:10.1117/1.3549928

Cited by 35 publications

(21 citation statements)

References 21 publications

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“…The first no-reference metric is the global color image contrast metric (ICM) that measures the global image contrast [78]. The ICM computes a weighted estimate of the dynamic ranges of both the graylevel and color luminance (L* in CIELAB L*a*b* color space) histograms.…”

Section: Methodsmentioning

confidence: 99%

“…The second no-reference metric is the color colorfulness metric (CCM) that measures the color vividness of an image as a weighted combination of color saturation and color variety [78]. Larger CCM values correspond to more colorful images.…”

Section: Methodsmentioning

confidence: 99%

“…The second full-reference metric is the color natural metric (CNM: [78,81,82]). The CNM measures the similarity of the color distributions of a color fused image and a daylight reference color image in Lab color space using Ma's [83] gray relational coefficients.…”

Section: Methodsmentioning

confidence: 99%

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Improved Color Mapping Methods for Multiband Nighttime Image Fusion

Hogervorst¹,

Toet²

2017

J. Imaging

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Previously, we presented two color mapping methods for the application of daytime colors to fused nighttime (e.g., intensified and longwave infrared or thermal (LWIR)) imagery. These mappings not only impart a natural daylight color appearance to multiband nighttime images but also enhance their contrast and the visibility of otherwise obscured details. As a result, it has been shown that these colorizing methods lead to an increased ease of interpretation, better discrimination and identification of materials, faster reaction times and ultimately improved situational awareness. A crucial step in the proposed coloring process is the choice of a suitable color mapping scheme. When both daytime color images and multiband sensor images of the same scene are available, the color mapping can be derived from matching image samples (i.e., by relating color values to sensor output signal intensities in a sample-based approach). When no exact matching reference images are available, the color transformation can be derived from the first-order statistical properties of the reference image and the multiband sensor image. In the current study, we investigated new color fusion schemes that combine the advantages of both methods (i.e., the efficiency and color constancy of the sample-based method with the ability of the statistical method to use the image of a different but somewhat similar scene as a reference image), using the correspondence between multiband sensor values and daytime colors (sample-based method) in a smooth transformation (statistical method). We designed and evaluated three new fusion schemes that focus on (i) a closer match with the daytime luminances; (ii) an improved saliency of hot targets; and (iii) an improved discriminability of materials. We performed both qualitative and quantitative analyses to assess the weak and strong points of all methods.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Improved Color Mapping Methods for Multiband Nighttime Image Fusion

Hogervorst¹,

Toet²

2017

J. Imaging

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“…Both the dynamic range of intensity level or the overall intensity distribution of the image can be provided by a histogram. A global contrast metric 19 is proposed using the histogram character. The histogram of image with levels in the range [0, N − 1] is a frequency-distribution function defined as the overall intensity distribution of an image…”

Section: Image Contrast Metricmentioning

confidence: 99%

“…To objectively assess a color-fusion method, Tsagaris 18 proposed a color image fusion measure (CIFM) by using the amount of common information between the source images and the colorized image, and also the distribution of color information. Yuan et al 19 presented an objective evaluation method for visible and infrared color fusion utilizing four metrics: image sharpness metric, image contrast metric, color colorfulness metric, and color naturalness metric. In this paper, we introduce an objective evaluation index (OEI) to quantitatively evaluate the colorized images.…”

Section: Introductionmentioning

confidence: 99%

Qualitative and quantitative comparisons of multispectral night vision colorization techniques

Zheng

Dong²,

Blasch

2012

Opt. Eng

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Abstract. Multispectral images enable robust night vision (NV) object assessment over day-night conditions. Furthermore, colorized multispectral 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 (objective) 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 truecolor daylight target picture (reference). Four coloring-mapping methods-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 experimental 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, objective 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; histogram 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.

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