Bionic Algorithm for Color Fusion of Infrared and Low Light Level Image Based on Rattlesnake Bimodal Cells

Night vision technology is becoming ever-more widely used in military and civil fields, and it will be more accurate for target detection and recognition through color fusion of infrared and low light level images. Since the classic Waxman fusion model-only simulates the rattlesnake's IR-depres...

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Vydáno v:IEEE access Ročník 6; s. 68981 - 68988
Hlavní autoři: Zhang, Zhen, Li, Huiqi, Zhao, Guoru
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
bimodal cell
Biomedical imaging
Biomembranes
bionic
Bionics
Color
color fusion
Image color analysis
Image enhancement
Image quality
Infrared image
Infrared imagery
Light levels
Low light level
low light level image
Mathematical model
Mathematical models
Neural networks
Night vision
Object recognition
Quality assessment
Target detection
Target recognition
Visualization
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:Night vision technology is becoming ever-more widely used in military and civil fields, and it will be more accurate for target detection and recognition through color fusion of infrared and low light level images. Since the classic Waxman fusion model-only simulates the rattlesnake's IR-depressed Visual Cell and the target in fusion image is not obvious, a novel fusion model is proposed in this paper. We enhance the edge information through the ON neural network for the infrared and low light-level images and then establish a mathematical model to process the rattlesnake's "enhanced cells" and "depressed cells". Next, we input the ON-central receptive field for fusion and RGB spatial mapping, which can fully realize the union function of the "enhanced cells" and "depressed cells". Finally, we conduct comparative experiments and image quality evaluation with the classical Waxman fusion model. The results show that image targets are more obvious obtained by our algorithm and increased by an average of 51.97%, 4.07%, and 7.62% than Waxman algorithm in terms of color, mutual information, and structural similarity, respectively. It turned out that our fusion images are richer in color than the Waxman fusion images, which contain more source image information, and more similar to the source image structure.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2880845