Advanced Search
中文
Volume 44 Issue 5
May  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
REN Quanhui, SUN Yijie, HUANG Cansheng. Infrared and Visible Image Fusion Algorithm Based on Regional Similarity[J]. Infrared Technology , 2022, 44(5): 492-496.
Citation: REN Quanhui, SUN Yijie, HUANG Cansheng. Infrared and Visible Image Fusion Algorithm Based on Regional Similarity[J]. Infrared Technology , 2022, 44(5): 492-496.
shu

Infrared and Visible Image Fusion Algorithm Based on Regional Similarity

  • 1.

    Henan Province Smart Education and Intelligent Technology Application Engineering Technology Research Center, Zhengzhou 451460, China

  • 2.

    College of Mathematics, Physics and Electronic Information Engineering, Guangxi Normal University for Nationalities, Chongzuo 532200, China

More Information
  • Received Date: August 28, 2021
  • Revised Date: November 22, 2021
    Graphical Abstract
    • Abstract
  • To address the problems of local blur and incomplete background information in the traditional fusion algorithm of infrared and visible images, a new fusion algorithm is proposed in this paper. The edge detection operator was used to extract the image contour, and weighted fusion based on energy was also executed. The similarity between regions was used to extract the signal domain. Finally, image fusion is performed according to the over-signal strength. To verify the correctness of the algorithm, a comparative test was conducted and a quantitative analysis was performed using three parameters: standard deviation, information entropy, and average gradient. Compared with the traditional weighted average algorithm, the standard deviation of this method was up to 106.3 %. The test results confirmed that the fusion method proposed in this study has a better fusion effect and practical value.
    • FullText(HTML)
    • References (15)
  • [1]
    MA J, MA Y, LI C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 2019, 45: 153-178. DOI: 10.1016/j.inffus.2018.02.004
    [2]
    LIU Y, CHEN X, Ward R K, et al. Medical image fusion via convolutional sparsity based morphological component analysis[J]. IEEE Signal Processing Letters, 2019, 26(3): 485-489. DOI: 10.1109/LSP.2019.2895749
    [3]
    蔡铠利, 石振刚. 红外图像与可见光图像融合算法研究[J]. 沈阳理工大学学报, 2016(3): 17-22. DOI: 10.3969/j.issn.1003-1251.2016.03.004

    CAI Kaili, SHI Zhengang. Research on Image Fusion Algorithm of Infrared and Visible Image[J]. Journal of Shenyang Ligong University, 2016(3): 17-22. DOI: 10.3969/j.issn.1003-1251.2016.03.004
    [4]
    郝志成, 吴川, 杨航, 等. 基于双边纹理滤波的图像细节增强方法[J]. 中国光学, 2016, 9(4): 423-431. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGA201604005.htm

    HAO Zhicheng, WU Chuan, YANG Hang, et al. Image detail enhancement method based on multi-scale bilateral texture filter[J]. Chinese Optics, 2016, 9(4): 423-431. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGA201604005.htm
    [5]
    FU Z, WANG X, LI X, et al. Infrared and visible image fusion based on visual saliency and NSCT[J]. Journal of University of Electronic Science & Technology of China, 2017, 46(2): 357-362.
    [6]
    DING S, ZHAO X, HUI X, et al. NSCT-PCNN image fusion based on image gradient motivation[J]. IET Computer Vision, 2018, 12(4): 377-383. DOI: 10.1049/iet-cvi.2017.0285
    [7]
    KOU F, LI Z, WEN C, et al. Edge-Preserving smoothing pyramid based multi-scale exposure fusion[J]. Journal of Visual Communication & Image Representation, 2018, 53: 235-244.
    [8]
    ZHOU Z, BO W, SUN L, et al. Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters[J]. Information Fusion, 2016, 30: 15-26. DOI: 10.1016/j.inffus.2015.11.003
    [9]
    YANG B, LUO J, GUO L, et al. Simultaneous image fusion and demosaicing via compressive sensing[J]. Information Processing Letters, 2016, 116(7): 447-454. DOI: 10.1016/j.ipl.2016.03.001
    [10]
    ZHANG Y, BAI X, WANG T. Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure[J]. Information Fusion, 2017, 35: 81-101. DOI: 10.1016/j.inffus.2016.09.006
    [11]
    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.
    [12]
    YANG Y, QUE Y, HUANG S, et al. Multiple visual features measurement with gradient domain guided filtering for multisensor image fusion[J]. IEEE Transactions on Instrumentation & Measurement, 2017, 66(4): 691-703.
    [13]
    ZHANG L, ZENG G, WEI J. Adaptive region-segmentation multi-focus image fusion based on differential evolution[J]. International Journal of Pattern Recognition & Artificial Intelligence, 2018, 33(3): 32.
    [14]
    YAN X, QIN HL, LI J, et al. Infrared and visible image fusion using multiscale directional nonlocal means filter[J]. Applied Optics, 2015, 54(13): 4299-4308. DOI: 10.1364/AO.54.004299
    [15]
    CUI G M, FENG H J, XU Z H, et al. Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition[J]. Optics Communications, 2015, 341: 199-209. DOI: 10.1016/j.optcom.2014.12.032
    • Related Articles

  • Related Articles

    [1]WANG Wenjin, KONG Jincheng, QI Wenbin, ZHANG Yang, SONG Linwei, WU Jun, ZHAO Wen, YU Jianyun, QIN Gang. Research Progress on Materials and Devices of HgCdTe p-on-n Double Layer Heterojunction Grown by VLPE[J]. Infrared Technology , 2024, 46(3): 233-245.
    [2]YANG Chunzhang, QIN Gang, LI Yanhui, LI Da, KONG Jincheng. Research on Growth of M/L-wavelength Dual-band IR-MCT on CZT Substrate by MBE[J]. Infrared Technology , 2018, 40(1): 1-5.
    [3]ZHOU Lianjun, HAN Fuzhong, BAI Piji, SHU Chang, SUN Hao, WANG Xiaojuan, LI Jinghui, ZOU Pengcheng, GUO Jianhua, WANG Qiongfang. Review of HOT MW Infrared Detector Using MCT Technology[J]. Infrared Technology , 2017, 39(2): 116-124.
    [4]QIN Gang, LI Dongsheng, LI Xiongjun, LI Yanhui, WANG Xiangqian, YANG Yan, TIE Xiaoying, ZUO Dafan, BO Junxiang. Research on the Technique of in-situ p-on-n MWIR-MCT by MBE[J]. Infrared Technology , 2016, 38(10): 820-824.
    [5]WANG Yi-feng, LI Pei-zhi, LIU Li-ming, WANG Dan-lin. Developments of Very Long Wavelength Mercury Cadmium Telluride Infrared Detectors[J]. Infrared Technology , 2012, 34(7): 373-382. DOI: 10.3969/j.issn.1001-8891.2012.07.001
    [6]Developments of Mercury Cadmium Telluride in Recent Years[J]. Infrared Technology , 2009, 31(8): 435-442. DOI: 10.3969/j.issn.1001-8891.2009.08.001
    [7]The Determination of Cadmium-Mercury Telluride Composition for Any Thickness by Infrared Transmission[J]. Infrared Technology , 2005, 27(1): 39-41. DOI: 10.3969/j.issn.1001-8891.2005.01.009
    [8]Measurement on Minority Carrier Lifetime of Mercury Cadmium Telluride Material by Microwave Photoconductivity Decay Method[J]. Infrared Technology , 2003, 25(6): 42-44,48. DOI: 10.3969/j.issn.1001-8891.2003.06.012
    [9]p+n Infrared Detectors by As Ion Implantation in HgCdTe[J]. Infrared Technology , 2002, 24(4): 46-48,26. DOI: 10.3969/j.issn.1001-8891.2002.04.012
    [10]The Surface Passivation of MCT Infrared Detectors[J]. Infrared Technology , 2001, 23(3): 9-12,15. DOI: 10.3969/j.issn.1001-8891.2001.03.003

  • Cited by

    Periodical cited type(4)

    1. 王振，刘磊. 基于改进分水岭算法的电力设备红外图像分割. 红外技术. 2025(04): 484-492 . 本站查看
    2. 刘沛津，张香瑞，魏平. 基于融合重构的电气设备红外图像EnFCM聚类分割方法. 红外技术. 2024(03): 295-304 . 本站查看
    3. 张利军. 基于红外热成像技术的变电站巡检机器人的应用. 山东煤炭科技. 2024(12): 168-172 .
    4. 冯杰，张莹，叶影，贺润平，王哲斐. NSST域电气设备红外图像增强处理算法设计. 电子设计工程. 2023(21): 176-179+185 .

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML
    Article views PDF downloads Cited by(7)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return