| 光谱学与光谱分析 |
|
|
|
|
|
| Visible and Infrared Thermal Image Fusion Algorithm Based on Self-Adaptive Reference Image |
| LIU Jia-ni, JIN Wei-qi*, LI Li, WANG Xia |
| MOE Key Laboratory of Photoelectronic Imaging Technology and System, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China |
|
|
|
|
Abstract The technology of fusing visible images and infrared thermal images is one of the most important researching fields in terms of high-performance night vision technology home and abroad Using this kind of technology can efficiently improve people’s ability of target-detecting and scene’s comprehension. The commonly used color transfer algorithm belongs to single reference image’s global color transfer, which results in huge influence on the color of fusion image. Thus, this kind of method cannot adapt to different kinds of scenes. In this essay, we focus on environmental adaptation problems of regular color transfer in color image fusion algorithm base on YUV color space. After analyzing the mean and standard deviation of typical scenes’ UV channel, we find that it has obvious classified characteristics. As a result, we propose a method of constructing adaptive reference image based on mean and standard deviation of U and V channels, which makes visible and infrared thermal image fusion algorithm better adapt to environment and fusion images’ color is natural. Furthermore, the handling capacity is small and it exerts little impact on the speed of algorithm real-time hardware processing. To conclude, it is a kind of practical natural color fusion processing algorithm.
|
|
Received: 2015-11-24
Accepted: 2016-03-02
|
|
|
|
Corresponding Authors:
JIN Wei-qi
E-mail: jinwq@bit.edu.cn
|
|
[1] MAO Shi-yi, ZHAO Wei(毛士艺, 赵 巍). Journal of Beijing University of Aeronautics and Astronautics(北京航空航天大学学报)，2002, 28(5): 512.
[2] HU Chuan-qi, WANG Xi, HOU Jia-huai, et al(胡传奇, 王 檄, 侯家槐，等). Geomatics & Spatial Information Technology(测绘与空间地理信息), 2010, 33(2): 159.
[3] Terashima, S, Ogawa, K. Brain Research, 2002，958：468471.
[4] LUO Yuan, WANG Ling-xue, JIN Wei-qi, et al(骆 媛, 王岭雪, 金伟其, 等). Infrared Technology(红外技术), 2010, 32(6): 337.
[5] HE Gui-qing, CHEN Shi-hao, TIAN Yun, et al(何贵青, 陈世浩, 田 沄，等). Chinese Journal of Computers(计算机学报), 2008, 31(3): 486.
[6] QIN Zheng, BAO Fu-min, LI Ai-guo, et al(覃 征, 鲍复民, 李爱国, 等). Microelectronics & Computer(微电子学与计算机), 2004, 21(2): 1.
[7] Toet A, Walraven J. Optical Engineering, 1996, 35(3): 650.
[8] Reinhard E, Ashikhmin M, Gooch B, et al. IEEE Computer Graphics and Applications, 2001, 21(5): 34.
[9] Toet A. Information Fusion, 2003, 4(3): 155.
[10] Toet A. International Society for Optics and Photonics, 2003. 168.
[11] Toet A, Hogervorst M A. Optical Engineering, 2012, 51(1): 010901.
[12] WANG Ling-xue, SHI Shi-ming, JIN Wei-qi, et al(王岭雪, 史世明, 金伟其, 等). Journal of Beijing Institute of Technology(北京理工大学学报), 2007, 27(3): 189.
[13] MA Shuang, FANG Jian-an, SUN Shao-yuan, et al(马 爽, 方建安, 孙韶媛, 等). Acta Optica Sinica(光学学报), 2009, 29(6): 1502.
[14] HE Yong-qiang, WANG Qun, LI Guo-song, et al(何永强, 王 群, 李国松, 等). Laser & Infrared(激光与红外), 2012, 42(12): 1393. |
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
| [3] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [4] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [5] |
WANG Zhen-tao1, DAI Jing-min1*, YANG Sen2. Research on Multi-Spectral Thermal Imager Explosion Flame True
Temperature Field Measurment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3885-3890. |
| [6] |
WANG Zhen-ni1, KANG Zhi-wei1*, LIU Jin2, ZHANG Jie2. A Solar Spectral Doppler Redshift Velocity Measurement Method Based on Adaptive EMD-NDFT[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3475-3482. |
| [7] |
ZHENG Shu-yuan1, 2, HAI Yan1, 2, HE Meng-qi1, 2, WANG Jian-xiong1, 2. Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3509-3517. |
| [8] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
| [9] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
| [10] |
WANG Yi-ru1, GAO Yang2, 3, WU Yong-gang4*, WANG Bo5*. Study of the Electronic Structure, Spectrum, and Excitation Properties of Sudan Red Ⅲ Molecule Based on the Density Functional Theory[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2426-2436. |
| [11] |
CHEN Chao-yang1, 2, LIU Cui-hong1, 2, LI Zhi-bin3, Andy Hsitien Shen1, 2*. Alexandrite Effect Origin of Gem Grade Diaspore[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2557-2562. |
| [12] |
LI Shu-fei1, LI Kai-yu1, QIAO Yan2, ZHANG Ling-xian1*. Cucumber Disease Detection Method Based on Visible Light Spectrum and Improved YOLOv5 in Natural Scenes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2596-2600. |
| [13] |
ZHANG Jing, GUO Zhen, WANG Si-hua, YUE Ming-hui, ZHANG Shan-shan, PENG Hui-hui, YIN Xiang, DU Juan*, MA Cheng-ye*. Comparison of Methods for Water Content in Rice by Portable Near-Infrared and Visible Light Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2059-2066. |
| [14] |
LIU Mei-jun, TIAN Ning*, YU Ji*. Spectral Study on Mouse Oocyte Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1376-1380. |
| [15] |
CI Cheng-gang*, ZANG Jie-chao, LI Ming-fei*. DFT Study on Spectra of Mn-Carbonyl Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1434-1441. |
|
|
|
|
