&lt;title&gt;Progress on color night vision: visible/IR fusion, perception and search, and low-light CCD imaging&lt;/title&gt;

Waxman, Allen M.; Gove, Alan N.; Siebert, Michael C.; Fay, David A.; Carrick, James E.; Racamato, Joseph P.; Savoye, E. D.; Burke, Barry E.; Reich, R.; McGonagle, William H.; Craig, David

doi:10.1117/12.241025

Cited by 33 publications

(36 citation statements)

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“…The individual images used in this study correspond to successive frames from these time sequences. Corresponding visual and thermal frames were fused using an opponent color fusion technique developed by the MIT Lincoln Laboratory (Waxman et al, 1995;Waxman et al, 1996a;Waxman et al, 1996b;Waxman et al, 1996c;Waxman et al, 1997;Waxman et al, 1999). Grayscale fused images were obtained by taking the luminance component of the corresponding color fused images.…”

Section: Imagerymentioning

confidence: 99%

Cognitive Image Fusion and Assessment

Toet¹

2011

Image Fusion

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Section: Imagerymentioning

confidence: 99%

Cognitive Image Fusion and Assessment

Toet¹

2011

Image Fusion

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“…Although appropriately false-colored imagery is often helpful for human observers in improving their performance on scene classification and reaction time tasks, 6,7 inappropriate color mappings can also be detrimental to human performance. 5,8 A possible reason is lack of physical color constancy.…”

Section: Introductionmentioning

confidence: 99%

“…1 The fused multispectral imagery (in gray scale) can increase the reliability of interpretation 2,3 and supports machine analysis (computer vision), whereas the colorized multispectral imagery (in colors) improves observer situational awareness, 4 reaction time, 5 and perceptual analysis (human vision). 6 This paper focuses on the discussion and comparisons of several NV coloring methods using multispectral 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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“…There are strong evidences that the fused multispectral imagery (in gray scales) increases the reliability of interpretation (Rogers & Wood, 1990;Essock et al, 2001) and thus good for machine analysis (computer vision); whereas the colorized multispectral imagery improves observer performance and reaction times (Toet et al 1997;Varga, 1999;Waxman et al, 1996) and thus good for visual analysis (human vision). Imagine a nighttime navigation task that may be executed by an aircraft equipped with a multispectral imaging system.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a night vision colorization technique can produce colorized imagery with a naturalistic and stable color appearance by processing multispectral night-vision imagery. Although appropriately false-colored imagery is often helpful for human observers in improving their performance on scene classification, and reaction time tasks (Essock et al, 1999;Waxman et al, 1996), inappropriate color mappings can also be detrimental to human performance (Toet & IJspeert, 2001;Varga, 1999). A possible reason is lack of physical color constancy (Varga, 1999).…”

Section: Introductionmentioning

confidence: 99%

An Exploration of Color Fusion with Multispectral Images for Night Vision Enhancement

Zheng¹

2011

Image Fusion and Its Applications

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<title>Progress on color night vision: visible/IR fusion, perception and search, and low-light CCD imaging</title>

Cited by 33 publications

References 0 publications

Cognitive Image Fusion and Assessment

Cognitive Image Fusion and Assessment

Qualitative and quantitative comparisons of multispectral night vision colorization techniques

An Exploration of Color Fusion with Multispectral Images for Night Vision Enhancement

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