Visual and infrared dual-band false color image fusion method motivated by Land’s experiment

Guang-hua, Huang; Ni, Guoqiang; Zhang, Bin

doi:10.1117/1.2709851

Cited by 21 publications

(16 citation statements)

References 18 publications

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“…Testing results showed lower colorfulness compared to Toet et al methods but allowed target detection. 19,20 These results also confirmed that the reddish features in the fused image are pulled from the IR source while greenish objects are from the visible source.…”

Section: Color Fusion Of Ir and Visible Imagessupporting

confidence: 75%

“…This was the case of the other architectures developed by this team during the same study. Relying on Land's experiment on color constancy of human vision, 19 Huang et al proposed a new method to fuse visual and IR images and generate a false color image. Their proposed architecture is based on equal energy distribution assumption of colors reflected to eyes.…”

Section: Color Fusion Of Ir and Visible Imagesmentioning

confidence: 99%

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Bio-empirical mode decomposition: visible and infrared fusion using biologically inspired empirical mode decomposition

Sissinto

Ladeji-Osias

2013

Opt. Eng

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Abstract. Bio-EMD, a biologically inspired fusion of visible and infrared (IR) images based on empirical mode decomposition (EMD) and color opponent processing, is introduced. First, registered visible and IR captures of the same scene are decomposed into intrinsic mode functions (IMFs) through EMD. The fused image is then generated by an intuitive opponent processing the source IMFs. The resulting image is evaluated based on the amount of information transferred from the two input images, the clarity of details, the vividness of depictions, and range of meaningful differences in lightness and chromaticity. We show that this opponent processing-based technique outperformed other algorithms based on pixel intensity and multiscale techniques. Additionally, Bio-EMD transferred twice the information to the fused image compared to other methods, providing a higher level of sharpness, more natural-looking colors, and similar contrast levels. These results were obtained prior to optimization of color opponent processing filters. The Bio-EMD algorithm has potential applicability in multisensor fusion covering visible bands, forensics, medical imaging, remote sensing, natural resources management, etc.

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Section: Color Fusion Of Ir and Visible Imagessupporting

confidence: 75%

Section: Color Fusion Of Ir and Visible Imagesmentioning

confidence: 99%

Bio-empirical mode decomposition: visible and infrared fusion using biologically inspired empirical mode decomposition

Sissinto

Ladeji-Osias

2013

Opt. Eng

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“…Howard et al, 2000;Krebs et al, 1998;Li et al, 2004;Schuler et al, 2000;Scribner et al, 2003). More intuitive color schemes may be obtained through opponent processing through feedforward center-surround shunting neural networks similar to those found in vertebrate color vision (Aguilar et al, 1998;Aguilar et al, 1999;Fay et al, 2000a;Fay et al, 2000b;Huang et al, 2007;Warren et al, 1999;Waxman et al, 1995;Waxman et al, 1997;Waxman et al, 1998). Although this approach produces fused nighttime images with appreciable color contrast, the resulting color schemes remain rather arbitrary and are usually not strictly related to the actual daytime color scheme of the scene that is registered.…”

Section: Color Representation Of Fused Imagerymentioning

confidence: 66%

Cognitive Image Fusion and Assessment

Toet¹

2011

Image Fusion

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“…Simply mapping the signals from different nighttime sensors (sensitive in different spectral wavebands) to the individual channels of a standard RGB color display or to the individual components of a perceptually decorrelated color space (sometimes preceded by a principal component transform or followed by a linear transformation of the color pixels to enhance color contrast) usually results in imagery with an unrealistic color appearance [36,[43][44][45][46]. More intuitive color schemes may be obtained by opponent processing through feedforward center-surround shunting neural networks similar to those found in vertebrate color vision [47][48][49][50][51][52][53][54][55]. Although this approach produces fused nighttime images with appreciable color contrast, the resulting color schemes remain rather arbitrary and are usually not strictly related to the actual daytime color scheme of the scene that is registered.…”

Section: Introductionmentioning

confidence: 99%

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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Visual and infrared dual-band false color image fusion method motivated by Land’s experiment

Cited by 21 publications

References 18 publications

Bio-empirical mode decomposition: visible and infrared fusion using biologically inspired empirical mode decomposition

Bio-empirical mode decomposition: visible and infrared fusion using biologically inspired empirical mode decomposition

Cognitive Image Fusion and Assessment

Improved Color Mapping Methods for Multiband Nighttime Image Fusion

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