留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于滚动引导滤波的红外与可见光图像融合

张慧 韩新宁 韩惠丽

张慧, 韩新宁, 韩惠丽. 基于滚动引导滤波的红外与可见光图像融合[J]. 红外技术, 2022, 44(6): 598-603.
引用本文: 张慧, 韩新宁, 韩惠丽. 基于滚动引导滤波的红外与可见光图像融合[J]. 红外技术, 2022, 44(6): 598-603.
ZHANG Hui, HAN Xinning, HAN Huili. Infrared and Visible Image Fusion Based on a Rolling Guidance Filter[J]. Infrared Technology , 2022, 44(6): 598-603.
Citation: ZHANG Hui, HAN Xinning, HAN Huili. Infrared and Visible Image Fusion Based on a Rolling Guidance Filter[J]. Infrared Technology , 2022, 44(6): 598-603.

基于滚动引导滤波的红外与可见光图像融合

基金项目: 

固原市科技计划项目 2020GYKYF008

宁夏自然科学基金 2022AAC03331

固原市科技计划项目 2020GYKYF011

详细信息
    作者简介:

    张慧(1977-),女,宁夏固原人,宁夏师范学院数学与计算机科学学院,硕士,副教授。主要研究方向图形图像处理。E-mail:2466437143@qq.com

  • 中图分类号: TN911.7

Infrared and Visible Image Fusion Based on a Rolling Guidance Filter

  • 摘要: 为提高融合图像更加适应人类视觉感知,并解决可见光图像受光线、天气等影响而导致融合效果不佳的问题,本文提出了一种基于滚动引导滤波的可见光与红外图像融合方法。首先,利用引导滤波对可见光图像的内容进行增强,然后,利用滚动引导滤波将可见光和红外图像进行多尺度分解为小尺度层、大尺度层和基础层。在大尺度层的信息合成的过程中利用加权最小二乘法融合规则解决融合时可见光与红外图像不同特征带来的困扰,提高融合图像的视觉效果;在基础层的融合过程中采用优化的视觉显著图融合规则,减少对比度损失。最后,将大尺度层、小尺度层与基础层合并为融合后的图像。实验结果表明所给方法在提高视觉感知、细节处理、边缘保护等方面都有良好的效果。
  • 图  1  两组可见光图像的增强结果

    Figure  1.  Visibility enhancement results for two test visible images

    图  2  基于多尺度分解的滚动方向导波融合流程图

    Figure  2.  Flow chart of fusion based on MSD of the rolling guided filter

    图  3  测试集

    Figure  3.  Test set

    图  4  两组实验结果图

    Figure  4.  Two sets of experimental results

    表  1  用DWT、CVT、GFF、MGF、RGF_GS和RGF_GSE方法融合得到的指标

    Table  1.   Comparison with DWT, CVT, GFF, MGF, RGF_GS and RGF_GSE of different processing results

    Criteria DWT CVT GFF MGF RGF_GS RGF_GSE
    EN 7.0418 7.0821 6.7096 6.6521 6.6325 7.1212
    SD 42.9531 41.8259 34.9514 33.4429 36.0834 43.0536
    QTE 0.3753 0.3713 0.41158 0.3804 0.3964 0.41163
    QNCIE 0.80951 0.8083 0.8101 0.8059 0.8067 0.80952
    PS 21.4059 20.7318 19.7443 18.4000 19.5114 24.8152
    Time/s 2.03902 2.7364 1.5646 1.6532 1.9215 1.6604
    下载: 导出CSV
  • [1] ZHANG H, MA X, TIAN Y S. An image fusion method based on Curvelet transform and guided filter enhancement[J]. Mathematical Problems in Engineering, 2020(4): 1-8(DOI: 10.1155/2020/9821715)
    [2] 张慧, 常莉红, 马旭, 等. 一种基于曲波变换与引导滤波增强的图像融合方法[J]. 吉林大学学报: 理学版, 2020, 58(1): 113-119. https://www.cnki.com.cn/Article/CJFDTOTAL-JLDX202001018.htm

    ZHANG H, CHANG L H, MA X. An image fusion method based on Curvelet transform and guide filtering enhancement[J]. Journal of Jilin University: Science Edition, 2020, 58(1): 113-119. https://www.cnki.com.cn/Article/CJFDTOTAL-JLDX202001018.htm
    [3] 张慧, 常莉红. 基于方向导波增强的红外与可见光图像融合[J]. 激光与红外, 2020, 50(4): 508-512. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202004021.htm

    ZHANG H, CHANG L H. Infrared and visible image fusion based on guided filtering enhancement[J]. Laser & Infrared, 2020, 50(4): 508-512. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202004021.htm
    [4] LIU Y, LIU S, WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24: 147-164. doi:  10.1016/j.inffus.2014.09.004
    [5] LI Shutao, KANG Xudong, HU Jianwen. Image fusion with guided filtering[J]. IEEE Trans. Image Process, 2013, 22(7): 2864-2875. doi:  10.1109/TIP.2013.2244222
    [6] Bavirisetti D P, XIAO G, ZHAO H, et al. Multi-scale guided image and video fusion: a fast and efficient approach[J]. Circuits System Signal Process, 2019, 38: 5576-5605. doi:  10.1007/s00034-019-01131-z
    [7] GAN W, WU X, WU W, et al. Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter[J]. Infrared Physics & Technology, 2015, 72: 37-51.
    [8] ZHANG Q, SHEN X, XU L, et al. Rolling guidance filter[C]//European Conference on Computer Vision, Springer, 2014: 815-830.
    [9] 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.
    [10] ZHAI Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues[C]//Proceedings of the 14th ACM International Conference on Multimedia, ACM, 2006: 815-824.
    [11] Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEE Transactions on Image Processing, 2000, 9(5): 889-896. doi:  10.1109/83.841534
    [12] Rizzi A, Gatta C, Marini D. A new algorithm for unsupervised global and local color correction[J]. Pattern Recognition Letters, 2003, 24: 1663-1677. doi:  10.1016/S0167-8655(02)00323-9
    [13] ZHOU Z, WANG B, LI S, 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
    [14] DONG Z, LAI C, QI D, et al. A general memristor-based Pulse coupled neural network with variable linking coefficient for multi-focus image fusion[J]. Neurocomputing, 2018, 308: 172-183. doi:  10.1016/j.neucom.2018.04.066
    [15] ZHOU Z, DONG M, XIE X, et al. Fusion of infrared and visible images for night-vision context enhancement[J]. Applied Optics, 2016, 55(23): 6480-6489. doi:  10.1364/AO.55.006480
    [16] ZHAI Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues[C]//Proceedings of the 14th ACM International Conference on Multimedia, ACM, 2006: 815-824.
    [17] Nava R, Cristo´bal G, Escalante-Ramırez B. Mutual information improves image fusion quality assessments[EB/OL][2007-09-04]. https://spie.org/news/0824-mutual-information-improves-image-fusion-quality-assessments?SSO=1
    [18] WANG Q, SHEN Y, JIN J. Performance evaluation of image fusion techniques[J]. Image Fusion: Algorithms and Applications, 2008, 9(10): 469-492.
  • 加载中
图(4) / 表(1)
计量
  • 文章访问数:  216
  • HTML全文浏览量:  49
  • PDF下载量:  47
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-03-11
  • 修回日期:  2021-04-12
  • 刊出日期:  2022-06-20

目录

    /

    返回文章
    返回