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Volume 44 Issue 7
Jul.  2022
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JIANG Mai, SHA Guijun, LI Ning. Infrared and Low-level Visible Light Images Fusion Based on Perception Unified Color Space and Dual Tree Complex Wavelet Transform[J]. Infrared Technology , 2022, 44(7): 716-725.
Citation: JIANG Mai, SHA Guijun, LI Ning. Infrared and Low-level Visible Light Images Fusion Based on Perception Unified Color Space and Dual Tree Complex Wavelet Transform[J]. Infrared Technology , 2022, 44(7): 716-725.
shu

Infrared and Low-level Visible Light Images Fusion Based on Perception Unified Color Space and Dual Tree Complex Wavelet Transform

  • 1.

    Criminal Investigation and Counter-terrorism College, Criminal Investigation Police University of China, Shenyang 110854, China

  • 2.

    Marine Information Technology Equipment Centre, Shenyang Institute of Automation, Shenyang 110169, China

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  • Received Date: October 10, 2021
  • Revised Date: November 28, 2021
    Graphical Abstract
    • Abstract
  • To solve problems in traditional image fusion, such as dim targets, low contrast, and loss of edge and textural details in fusion results, a new fusion approach for infrared and low-level visible light image fusion based on perception unified color space (PUCS) and dual tree complex wavelet transform (DTCWT) is proposed. First, the two-source image intensity component is separately transformed from RGB space into PUCS to obtain a new intensity component for further processing. Then, the infrared and low-level visible light images are decomposed using DTCWT to obtain the low- and high-frequency components, respectively. Subsequently, at the fusion stage, the region energy adaptive weighted method is adopted to fuse the low-frequency sub-bands, and the high-frequency rule uses the sum modified Laplacian and gradient value vector for different scale and directional sub-bands fusions. Finally, the fusion image is obtained by applying inverse DTCWT on the sub-bands and returned to RGB space. The proposed algorithm was compared with three efficient fusion methods in different scenarios. The experimental results show that this approach can achieve prominent target characteristics, clear background texture and edge details, and suitable contrast in subjective evaluations as well as advantages in eight objective indicator evaluations.
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    • References (23)
  • [1]
    苏金凤, 张贵仓, 汪凯. 结合RPCA和NSCT的红外与可见光图像的压缩融合[J]. 激光与光电子学进展, 2020, 57(4): 04105. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202004008.htm

    SU Jinfeng, ZHANG Guicang, WANG Kai. Infrared and visible image compressed fusion combining RPCA and NSCT[J]. Laser & Optoelectronics Progress, 2020, 57(4): 04105. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202004008.htm
    [2]
    刘明君, 董增寿. 基于多特征的红外与可见光图像融合[J]. 激光杂志, 2019, 40(10): 81-85. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ201910017.htm

    LIU Mingjun, DONG Zengshou. Infrared and visible light image fusion based on multiple features[J]. Laser Journal, 2019, 40(10): 81-85. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ201910017.htm
    [3]
    袁金楼, 吴谨, 刘劲. 基于NSCT与DWT的压缩感知图像融合[J]. 红外技术, 2015, 37(11): 176-182. http://hwjs.nvir.cn/article/id/hwjs201511011

    YUAN Jinlou, WU Jin, LIU Jin. Image fusion based on compressed sensing of NSCT and DWT[J]. Infrared Technology, 2015, 37(11): 176-182. http://hwjs.nvir.cn/article/id/hwjs201511011
    [4]
    邓秋菊, 王宁. 非下采样轮廓波变换的红外与可见光图像融合[J]. 激光杂志, 2020, 41(4): 92-95. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202004018.htm

    DENG Qiuju, WANG Ning. Infrared and visible image fusion of non-down sampling wheel corridor wave transform[J]. Laser Journal, 2020, 41(4): 92-95. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202004018.htm
    [5]
    肖中杰. 基于NSCT红外与可见光融合算法优化研究[J]. 红外技术, 2017, 39(12): 1127-1130. http://hwjs.nvir.cn/article/id/hwjs201712010

    XIAO Zhongjie. Improved infrared and visible light image fusion algorithm based on NSCT[J]. Infrared Technology, 2017, 39(12): 1127-1130. http://hwjs.nvir.cn/article/id/hwjs201712010
    [6]
    杨晟炜, 张志华, 孔玲君, 等. 基于NSST与IHS的红外与彩色可见光图像融合[J]. 包装工程, 2019, 40(11): 194-201. https://www.cnki.com.cn/Article/CJFDTOTAL-BZGC201911030.htm

    YANG Shengwei, ZHANG Zhihua, KONG Lingjun, et al. Fusion of infrared and color visible images based on NSST and IHS[J]. Packaging Engineering, 2019, 40(11): 194-201. https://www.cnki.com.cn/Article/CJFDTOTAL-BZGC201911030.htm
    [7]
    王志社, 杨风暴, 彭智浩. 基于NSST和稀疏表示的多源异类图像融合方法[J]. 红外技术, 2015, 37(3): 210-217. http://hwjs.nvir.cn/article/id/hwjs201503008

    WANG Zhishe, YANG Fengbao, PENG Zhihao. Mulit-source heterogeneous image fusion based on NSST and sparse presentation[J]. Infrared Technology, 2015, 37(3): 210-217. http://hwjs.nvir.cn/article/id/hwjs201503008
    [8]
    叶华, 朱明旱, 王日兴. 红外和可见光图像互补融合的运动目标检测方法[J]. 红外技术, 2015, 37(8): 648-654. http://hwjs.nvir.cn/article/id/hwjs201508004

    YE Hua, ZHU Minghan, WANG Rixing. Fusion of complementary information from infrared and visual images for moving object detection[J]. Infrared Technology, 2015, 37(8): 648-654. http://hwjs.nvir.cn/article/id/hwjs201508004
    [9]
    赵春晖, 马丽娟, 邵国锋. 采用WA-WBA与改进INSCT的图像融合算法[J]. 电子与信息学报, 2014, 36(2): 304-311. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201402009.htm

    ZHAO Chunhui, MA Lijuan, SHAO Guofeng. An image fusion algorithm based on WA-WBA and improved non-subsampled contourlet transform[J]. Journal of Electronics & Information Technology, 2014, 36(2): 304-311. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201402009.htm
    [10]
    杜进楷, 陈世国. 基于双树复小波变换的自适应PCNN图像融合算法[J]. 红外技术, 2018, 40(10): 1002-1007. http://hwjs.nvir.cn/article/id/hwjs201810012

    DU Jinkai, CHEN Shiguo. Adaptive PCNN image fusion algorithm based on double tree complex wavelet transform[J]. Infrared Technology, 2018, 40(10): 1002-1007. http://hwjs.nvir.cn/article/id/hwjs201810012
    [11]
    王聪, 钱晨, 孙伟, 等. 基于SCM和CST的红外与可见光图像融合算法[J]. 红外技术, 2016, 38(5): 396-402. http://hwjs.nvir.cn/article/id/hwjs201605007

    WANG Cong, QIAN Chen, SUN Wei, et al. Infrared and visible images fusion based on SCM and CST[J]. Infrared Technology, 2016, 38(5): 396-402. http://hwjs.nvir.cn/article/id/hwjs201605007
    [12]
    钱小燕, 张天慈, 王帮峰, 等. 局部颜色映射的彩色夜视算法[J]. 中国图象图形学报, 2012, 17(5): 689-693. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201205012.htm

    QIAN Xiaoyan, ZHANG Tianci, WANG Bangfeng, et al. Color night vision algorithm based on local color mapping[J]. Journal of Image and Graphics, 2012, 17(5): 689-693. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201205012.htm
    [13]
    Philipp Urban, Mitchell R. Rosen, ROY S. Berns. Embedding non-euclidean color spaces into euclidean color spaces with minimal isometric disagreement[J]. J. Opt. Soc. Am. A, 2007, 24(6): 1516-1528. DOI: 10.1364/JOSAA.24.001516
    [14]
    Ingmar Lissner, Philipp Urban. Toward a unified color space for perception-based image processing[J]. IEEE Transactions on Image Processing, 2012, 21(3): 1153-1168. DOI: 10.1109/TIP.2011.2163522
    [15]
    Nick Kingsbury. Image processing with complex wavelets[J]. Philosopical Transactions of Royal Society of London A: Mathematical, Physical and Engineering Sciences, 1999, 357(1760): 2543-2560. DOI: 10.1098/rsta.1999.0447
    [16]
    朱攀, 刘泽阳, 黄战华. 基于DTCWT和稀疏表示的红外偏振与光强图像融合[J]. 光子学报, 2017, 46(12): 1210002-1-1210002-9. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201712028.htm

    ZHU Pan, LIU Zeyang, HUANG Zhanhua, Infrared polarization and intensity image fusion based on dual-tree complex wavelet transform and space representation[J]. Acta Photonica Sinica, 2017, 46(12): 1210002-1-1210002-9. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201712028.htm
    [17]
    沈瑜, 陈小朋, 杨倩. 多方向Laplacian能量和与tetrolet变换的图像融合[J]. 中国图象图形学报, 2020, 25(4): 0721-0731. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202004008.htm

    SHEN Yu, CHEN Xiaopeng, YANG Qian. Image fusion of multidirectional sum modified Laplacian and Tetrolet transform[J]. Journal of Image and Graphics, 2020, 25(4): 0721-0731. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202004008.htm
    [18]
    方静, 罗高鹏. 改进快速NSST的热烟羽红外与可见光图像融合[J]. 激光与红外, 2017, 47(7): 914-920. DOI: 10.3969/j.issn.1001-5078.2017.07.024

    FANG Jing, LUO Gaopeng. Infrared and visible images fusion of thermal plume based on rapid non-subsampled shearlet transform[J]. Laser & Infrared, 2017, 47(7): 914-920. DOI: 10.3969/j.issn.1001-5078.2017.07.024
    [19]
    MA J, ZHOU Z, WANG B, et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 2017, 82: 8-17.
    [20]
    ZHANG Yu, ZHANG Lijia, BAI Xiangzhi, et al. Infrared and visual Image fusion through infrared feature extraction and visual information preservation[J]. Infrared Physics & Technology, 2017, 83: 227-237.
    [21]
    ZHOU Zhiqiang, LI Sun, WANG Bo. Multi-scale weighted gradient-based fusion for multi-focus image[J]. Information Fusion, 2014, 20: 60-72. DOI: 10.1016/j.inffus.2013.11.005
    [22]
    董安勇, 苏斌, 赵文博, 等. 基于卷积稀疏表示的红外与可见光图像融合[J]. 激光与红外, 2018, 48(12): 1547-1553. DOI: 10.3969/j.issn.1001-5078.2018.12.018

    DONG Anyong, SU Bin, ZHAO Wenbo. Infrared and visible image fusion based on convolution sparse representation[J]. Laser & Infrared, 2018, 48(12): 1547-1553. DOI: 10.3969/j.issn.1001-5078.2018.12.018
    [23]
    周晨旭, 黄福珍. 基于BLMD和NSDFB算法的红外与可见光图像融合方法[J]. 红外技术, 2019, 41(2): 176-182. http://hwjs.nvir.cn/article/id/hwjs201902012

    ZHOU Chenxu, HUANG Fuzhen. Infrared and visible image fusion based on BLMD and NSDFB[J]. Infrared Technology, 2019, 41(2): 176-182. http://hwjs.nvir.cn/article/id/hwjs201902012
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