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27 September 2016 A robust color image fusion for low light level and infrared images
Chao Liu, Xiao-hui Zhang, Qing-ping Hu, Yong-kang Chen
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Proceedings Volume 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment; 96841D (2016) https://doi.org/10.1117/12.2243116
Event: Eighth International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), 2016, Suzhou, China
Abstract
The low light level and infrared color fusion technology has achieved great success in the field of night vision, the technology is designed to make the hot target of fused image pop out with intenser colors, represent the background details with a nearest color appearance to nature, and improve the ability in target discovery, detection and identification. The low light level images have great noise under low illumination, and that the existing color fusion methods are easily to be influenced by low light level channel noise. To be explicit, when the low light level image noise is very large, the quality of the fused image decreases significantly, and even targets in infrared image would be submerged by the noise. This paper proposes an adaptive color night vision technology, the noise evaluation parameters of low light level image is introduced into fusion process, which improve the robustness of the color fusion. The color fuse results are still very good in low-light situations, which shows that this method can effectively improve the quality of low light level and infrared fused image under low illumination conditions.
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Chao Liu, Xiao-hui Zhang, Qing-ping Hu, and Yong-kang Chen "A robust color image fusion for low light level and infrared images", Proc. SPIE 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment, 96841D (27 September 2016); https://doi.org/10.1117/12.2243116
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KEYWORDS
Image fusion
Infrared imaging
Infrared radiation
Image quality
Signal to noise ratio
Image enhancement
Image processing
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