Advanced Search
中文
Volume 43 Issue 5
May  2021
Turn off MathJax
Article Contents
Abstract
References
Related Articles
WANG Zhishe, JIANG Xiaolin, WU Yuanyuan, WANG Junyao. Visible and Infrared Image Fusion Based on Group K-SVD[J]. Infrared Technology , 2021, 43(5): 455-463.
Citation: WANG Zhishe, JIANG Xiaolin, WU Yuanyuan, WANG Junyao. Visible and Infrared Image Fusion Based on Group K-SVD[J]. Infrared Technology , 2021, 43(5): 455-463.
shu

Visible and Infrared Image Fusion Based on Group K-SVD

  • School of Applied Science, Taiyuan University of Science and Technology, Taiyuan 030024, China

More Information
  • Received Date: August 19, 2020
  • Revised Date: October 23, 2020
    Graphical Abstract
    • Abstract
  • In the traditional image fusion method based on sparse representation, image blocks are used as units for dictionary training and sparse decomposition. The representation ability of dictionary atoms for image features is insufficient if the internal connection between the image blocks is not considered. Moreover, the sparse coefficients are inaccurate. Therefore, a fused image is not desirable. In view of the abovementioned problem, this paper proposes a fusion method based on the group K-means singular value decomposition (K-SVD) for visible and infrared images. Considering the image non-local similarity, this method constructs a structure group matrix using similar image blocks, and then, dictionary training and sparse decomposition are performed in the units of the structure group matrix by group K-SVD. Thus, this method can effectively improve the representation ability of dictionary atoms and the accuracy of the sparse coefficients. The experimental results show that this method is superior to the traditional sparse fusion method in terms of subjective and objective evaluation.
    • FullText(HTML)
    • References (23)
  • [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]
    LI S, KANG X, FANG L, et al. Pixel-level image fusion: a survey of the state of the art[J]. Information Fusion, 2017, 33: 100-112. DOI: 10.1016/j.inffus.2016.05.004
    [3]
    Elguebaly T, Bouguila N. Finite asymmetric generalized Gaussian mixture models learning for infrared object detection[J]. Computer Vision and Image Understanding, 2013, 117(12): 1659-1671. DOI: 10.1016/j.cviu.2013.07.007
    [4]
    LI H, DING W, CAO X, et al. Image registration and fusion of visible and infrared integrated camera for medium-altitude unmanned aerial vehicle remote sensing[J]. Remote Sensing, 2017, 9(5): 441. DOI: 10.3390/rs9050441
    [5]
    LI S, YANG B, HU J. Performance comparison of different multi-resolution transforms for image fusion[J]. Information Fusion, 2011, 12(2): 74-84. DOI: 10.1016/j.inffus.2010.03.002
    [6]
    FU Z, WANG X, XU J, et al. Infrared and visible images fusion based on RPCA and NSCT[J]. Infrared Physical Technology, 2016, 77: 114-123. DOI: 10.1016/j.infrared.2016.05.012
    [7]
    ZHANG Q, LIU Y, Blum R, et al. Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: a review[J]. Information Fusion, 2018, 40: 57-75. DOI: 10.1016/j.inffus.2017.05.006
    [8]
    ZHANG Z, XU Y, YANG J, et al. A survey of sparse representation: Algorithms and Applications [J]. IEEE Access, 2015(3): 490-530. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7102696
    [9]
    YANG B, LI S. Multifocus image fusion and restoration with sparse representation[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(4): 884-892. DOI: 10.1109/TIM.2009.2026612
    [10]
    YU N, QIU T, BI F, et al. Image features extraction and fusion based on joint sparse representation[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 1074-1082. DOI: 10.1109/JSTSP.2011.2112332
    [11]
    LIU Y, WANG Z. Simultaneous image fusion and denoising with adaptive sparse representation[J]. IET Image Processing, 2014, 9(5): 347-357. http://ieeexplore.ieee.org/document/7095698
    [12]
    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
    [13]
    WANG Z, YANG F, PENG Z, et al. Multi-sensor image enhanced fusion algorithm based on NSST and top-hat transformation[J]. Optik, 2015, 126(23): 4184-4190. DOI: 10.1016/j.ijleo.2015.08.118
    [14]
    WANG Z, XU J, JIANG X, et al. Infrared and visible image fusion via hybrid decomposition of NSCT and morphological sequential toggle operator[J]. Optik, 2020, 201: 163497. DOI: 10.1016/j.ijleo.2019.163497
    [15]
    杨风暴. 红外偏振与光强图像的拟态融合原理和模型研究[J]. 中北大学学报: 自然科学版, 2017, 38(1): 1-7. DOI: 10.3969/j.issn.1673-3193.2017.01.001

    YANG Fengbao. Research on Theory and Model of Mimic Fusion Between Infrared Polarization and Intensity Images[J]. Journal of North University of China, 2017, 38(1): 1-7. DOI: 10.3969/j.issn.1673-3193.2017.01.001
    [16]
    LI H, WU X J. DenseFuse: A Fusion Approach to Infrared and Visible Images[J]. IEEE Transaction Image Processing, 2019, 28(5): 2614-2623. DOI: 10.1109/TIP.2018.2887342
    [17]
    MA J, YU W, LIANG P, et al. Fusion GAN: A generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 2019, 48: 11-26. DOI: 10.1016/j.inffus.2018.09.004
    [18]
    姜晓林, 王志社. 可见光与红外图像结构组双稀疏融合方法研究[J]. 红外技术, 2020, 42(3): 272-278. http://hwjs.nvir.cn/article/id/hwjs202003010

    JIANG Xiaolin, WANG Zhishe. Visible and Infrared Image Fusion Based on Structured Group and Double Sparsity[J]. Infrared Technology, 2020, 42(3): 272-278. http://hwjs.nvir.cn/article/id/hwjs202003010
    [19]
    DONG W, LEI Z, SHI G. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2012, 22(4): 1620-1630. http://ieeexplore.ieee.org/document/6392274/
    [20]
    ZHANG J, ZHAO D, GAO W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3336-3351. DOI: 10.1109/TIP.2014.2323127
    [21]
    WANG Z, Bovik A, A universal image quality index[J]. IEEE Signal Processing Letters. 2002, 9(3): 81–84. DOI: 10.1109/97.995823
    [22]
    Piella G, Heijmans H. A new quality metric for image fusion[C]//Proceedings of the 10th International Conference on Image Processing, 2003: 173-176.
    [23]
    Xydeas C. S, Petrovic V, Objective image fusion performance measure[J]. Electronics Letters, 2000, 36(4): 308-309. DOI: 10.1049/el:20000267
    • Related Articles

  • Related Articles

    [1]ZHAO Qiang, LIU Shengjie, HAN Dongcheng, LIU Changyu, YANG Shizhi. Improved K-means Clustering-based Defect Detection Method for Photovoltaic Panels[J]. Infrared Technology , 2024, 46(4): 475-482.
    [2]ZHANG Qingyu, FAN Yugang, GAO Yang. Defect Detection of Eddy-Current Thermography Based on Single-Scale Retinex and Improved K-means Clustering[J]. Infrared Technology , 2020, 42(10): 1001-1006.
    [3]WANG Lingzhi, LEI Zhenggang, ZHOU Hao, YU Chunchao, YANG Zhixiong, DUAN Shaoli, NIE Dong. Long-wave Infrared Hyperspectral Image Classification Based on K-means of Spatial-Spectral Features[J]. Infrared Technology , 2020, 42(4): 348-355.
    [4]JIANG Xiaolin, WANG Zhishe. Visible and Infrared Image Fusion Based on Structured Group and Double Sparsity[J]. Infrared Technology , 2020, 42(3): 272-278.
    [5]SU Hongchao, HU Ying, HONG Shaozhuang. Edge Detection Based on Characteristics of Infrared Image and K-means[J]. Infrared Technology , 2020, 42(1): 81-85.
    [6]WU Tianai, HUANG Shucai, YUAN Zhiwei, WU Yunrong, FENG Hui. NSCT Combined with SVD for Infrared Dim Target Complex Background Suppression[J]. Infrared Technology , 2016, 38(9): 758-764.
    [7]Parameter Extraction of Extrinsic Capacitance of MOSFET at 77 K Cryogenic Temperature[J]. Infrared Technology , 2013, (1): 9-15.
    [8]ZHAO Jing-yuan, WANG Li-ming, LIU Bin. The Research of Infrared Image Sequence Enhancement Based on SVD Algorithm[J]. Infrared Technology , 2009, 31(1): 47-50. DOI: 10.3969/j.issn.1001-8891.2009.01.013
    [9]LI Hui, QIAN Yun-sheng, CHANG Ben-kang, LIU Lei, XIA Yang, LI Shi-yi. The Research of K Factor for Signal-to-noise Ratio of LLLIntensifier[J]. Infrared Technology , 2007, 29(8): 488-490. DOI: 10.3969/j.issn.1001-8891.2007.08.015
    [10]ZHENG Lie-hua, YIN Da-yi, FENG Xin. Application for Offsetting Image Rotation "K Mirror" in COCTS[J]. Infrared Technology , 2007, 29(1): 17-21. DOI: 10.3969/j.issn.1001-8891.2007.01.005

  • Cited by

    Periodical cited type(1)

    1. 曹世超，刘晓营，梁舒. 基于双密度双树复小波变换的红外图像降噪算法. 邢台职业技术学院学报. 2022(03): 93-97 .

    Other cited types(5)

Catalog

    Get Citation
    PDF
    XML
    Article views (598) PDF downloads (73) Cited by(6)
    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