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

图像融合质量客观评价方法

高绍姝 金伟其 王岭雪 王吉晖 王霞

高绍姝, 金伟其, 王岭雪, 王吉晖, 王霞. 图像融合质量客观评价方法[J]. 应用光学, 2011, 32(4): 671-677.
引用本文: 高绍姝, 金伟其, 王岭雪, 王吉晖, 王霞. 图像融合质量客观评价方法[J]. 应用光学, 2011, 32(4): 671-677.
GAO Shao-shu, JIN Wei-qi, WANG Ling-xue, WANG Ji-hui, WANG xia. Objective quality assessment of image fusion[J]. Journal of Applied Optics, 2011, 32(4): 671-677.
Citation: GAO Shao-shu, JIN Wei-qi, WANG Ling-xue, WANG Ji-hui, WANG xia. Objective quality assessment of image fusion[J]. Journal of Applied Optics, 2011, 32(4): 671-677.

图像融合质量客观评价方法

详细信息
    通讯作者:

    高绍姝(1983-),女,山西省人,博士研究生,主要从事光电图像与系统质量评价研究工作。

  • 中图分类号: TN911.73

Objective quality assessment of image fusion

  • 摘要: 随着图像融合技术的快速发展,科学的图像融合质量客观评价对选择合理的融合算法以及研究新的融合算法具有重要的指导意义,成为图像质量评价研究的热点。理想的图像质量客观评价方法可以给出与人主观感受相一致的量化指标。对目前已提出的多种图像融合质量客观评价算法进行综述。简要介绍了基本的客观评价指标;将基于边缘保持度、基于结构相似度、基于信息论以及基于对比度的4类客观评价算法分别进行了介绍,着重分析和比较了各类算法的思路及特点;总结融合图像质量客观评价算法的研究趋势,指出无参考图像与彩色融合图像的质量评价方法是未来重要的发展方向。
  • [1]TOET A, IJSPEERT J K, WAXMAN A M, et al. Fusion of visible and thermal imagery improves situational awareness[J]. Displays, 1997, 18 (2): 85-95.
    [2]RILEY T, SMITH M. Image fusion technology for security and surveillance applications[J]. Optics and Photonics for Counterterrorism and Crime Fighting II, The International Society for Optical Engineering, 2006, 6402: 640204.
    [3]TOET A. Color image fusion for concealed weapon detection[J]. SPIE, 2003, 5071: 372-380.
    [4]ZOU X, BHANU B. Tracking humans using multi-modal fusion[J]. IEEE, 2005, 25 (25): W01-30-1-W01-308.
    [5]KONG S G, HEO J, BOUGHORBEL F, et al. Multiscale fusion of visible and thermal ir images for illumination-invariant face recognition[J]. International Journal of Computer Vision, 2007, 71 (2): 215-233.
    [6]DAS S, ZHANG Y. Color night vision for navigation and surveillance[J]. Transportation Research Record, 2000, 1708: 40-46.
    [7]TOET A, HOGERVORST M A, NIKOLOV S G, et al. Towards cognitive image fusion[J]. Information Fusion, 2010, 11 (2): 95-113.
    [8]KING B, LEUNG L W. Comparison of image data fusion techniques using entropy and INI[J]. 22nd Asian Conference on Remote Sensing, 2001, 5 (9): 132-136.
    [9]康圣, 王江安, 宗思光,等.图像融合的量化评价方法及实验分析[J]. 光电子技术与信息,2006,19(2): 59-63.
    KANG Sheng, WANG Jiang-an, ZONG Si-guang, et al. Objective evaluation and experimental analysis of multisensor image fusion[J]. Optoelectronic Technology and Information, 2006, 19 (2): 59-63. (in Chinese with an English abstract)
    [10]宋乐. 异源图像融合及其评价方法的研究[D]. 天津:天津大学, 2008.
    SONG Le. Research on the method for different-source image fusion and its evaluation[D]. Tianjin:Tianjin University, 2008. (in Chinese)


    [11]杨威, 赵剡, 许东. 基于人眼视觉的结构相似度图像质量评价方法[J]. 北京航空航天大学学报, 2008, 34(1): 1-4.
    YANG Wei, ZHAO Yan, XU Dong. Method of image quality assessment based on human visual system and structural similarity[J]. Journal of Beijing University of Aeronautics and Aastronautics, 2008, 34(1): 1-4. (in Chinese with an English abstract)
    [12]XYDEAS C, PETROVIC V. Objective image fusion performance measure[J]. Electronics Letters, 2000, 36 (4): 308-309.
    [13]XYDEAS C, PETROVIC V. Objective pixel-level image fusion performance measure[J]. SPIE, 2000, 4051: 89-99.
    [14]PETROVIC V, XYDEAS C. Sensor noise effects on signal-level image fusion performance[J]. Information Fusion, 2003, 4(3): 167-183.
    [15]PETROVIC V, XYDEAS C. Evaluation of image fusion performance using visible differences[J]. Lecture Notes in Computer Science, Computer Vision ECCV, 2004, 3023: 380-391.
    [16]WANG Z , BOVIK A C. A Universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3): 81-83.
    [17]WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error measurement to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
    [18]WANG Z, SIMONCELLI E P, BOVIK A C. Multi-scale structural similarity for image quality assessment[J]. Signals, Systems and Computers, IEEE, 2003, 2: 1398-1402.
    [19]PIELLA G, HEIJMANS H A. A new quality metric for image fusion[J]. International Conference on Image Processing, IEEE, 2003, 2: 173-176.
    [20]PIELLA G. New quality measures for image fusion[C]. Proc. of the 7th International Conference on Information Fusion, Sweden:Stockholm, 2004, 542-546.
    [21]CVEJIC N, ?OZA A, BULL D, et al. A similarity metric for assessment of image fusion algorithms[J]. International Journal of Signal Processing , 2005, 2(3):178-182.
    [22]YANG Cui, ZHANG Jian-qi, WANG Xiao-rui, et al. A novel similarity based quality metric for image fusion[J]. Information Fusion, 2008, 9(2): 156-160.
    [23]ZHENG You-zhi, QIN Zheng. Objective image fusion quality evaluation using structural similarity[J]. Tsinghua Science and Technology, 2009, 14(6): 703-709.
    [24]QU G, ZHANG D, YAN P. Information measure for performance of image fusion[J]. Electronics Letters, 2002, 38 (7): 313-315.
    [25]RAMESH C , RANJITH T. Fusion performance measures and a lifting wavelet transform based algorithm for image fusion[J]. Information Fusion, 2002, 1: 317-320.
    [26]WANG Qiang, SHEN Yi, ZHANG Ye, et al. A quantitative method for evaluating the performances of hyperspectral image fusion[J]. IEEE Transactions on Instrumentation and Measurement. 2003, 52(4): 1041-1047.
    [27]WANG Qiang, ZHANG Ye, LI Shuo, et al. A quantitative and comparative analysis of hyperspectral data Fusion Performance[J]. Journal of Harbin Institute of Technology (New Series), 2002, 9(3): 234-238.
    [28]WANG Qiang, SHEN Yi, ZHANG Ye. A quantitative method to evaluate the performance of hyperspectral Data fusion[J]. IEEE, 2002, 2:919-923.
    [29]WANG Qiang, SHEN Yi, ZHANG Ye. A fast method to evaluate the performances of image fusion techniques and its error analysis[J]. IEEE, 2003, 1: 823-826.
    [30]WANG Qiang, SHEN Yi. Performances evaluation of image fusion techniques based on nonlinear correlation measurement[J]. IEEE, 2004, 1: 472-475.
    [31]WANG Qiang, SHEN Yi, ZHANG Ye,et al. Fast quantitative correlation analysis and information deviation analysis for evaluating the performances of image fusion techniques[J]. IEEE Transactions on Instrumentation and Measurement,2004, 53(5): 1441-1447.
    [32]TSAGARIS V, ANASTASSOPOULOS V. A global measure for assessing image fusion methods[J]. Optical Engineering, 2006, 45(2): 026201.
    [33]TSAGARIS V, ANASTASSOPOULOS V. Information measure for assessing pixel-level fusion methods[J]. SPIE, 2004, 5573: 64-71.
    [34]TSAGARIS V. Objective evaluation of color image fusion methods[J]. Optical Engineering, 2009, 48(6): 066201.
    [35]TSAGARIS V, ANASTASSOPOULOS V. Assessing information content in color images[J]. Journal of Electronic Imaging, 2005, 14(4): 043007.
    [36]SHI J S, JIN W Q, WANG L X, et al. Objective evaluation of color fusion of visual and IR imagery by measuring image contrast[J]. SPIE, 2005, 5640: 594-601.
    [37]PELI E. Contrast in complex images[J]. J. Optical Soc. Am A, 1990, 7 (10): 2032-2040.
    [38]NADENAU M. Integration of human colour vision models into high quality image compression[D].Cole Switzerland: Polytechnique Fédérale de Lausanne,  2000.
    [39]CHEN Y, BLUM R S. A new automated quality assessment algorithm for image fusion[J]. Image and Vision Computing, 2009, 27 (10): 1421-1432.
    [40]CHEN Y, BLUM R S. A new automated quality assessment algorithm for night vision image fusion[J]. IEEE, 2007, 14: 518-523.
     
     
     


    (上接第640页)


    [8]苏奎峰, 吕强, 耿庆锋, 等. TMS320F2812原理与开发[M]. 北京: 电子工业大学出版社, 2005.
    SU Kui-feng, LU Qiang, GENG Qing-feng, et al. TMS320F2812 principles and development[M]. Beijing: Electronic Industry University Press, 2005. (in Chinese)
    [9]薛定宇, 陈阳泉. 基于MATLAB/Simulink的系统仿真技术与应用[M]. 北京: 清华大学出版社, 2002.
    XUE Ding-yu, CHEN Yang-quan. Simulation technology and application based on MATLAB/Simulink system[M]. Beijing: Tsinghua University Press, 2002. (in Chinese)
  • 加载中
计量
  • 文章访问数:  9062
  • HTML全文浏览量:  183
  • PDF下载量:  1351
  • 被引次数: 0
出版历程
  • 刊出日期:  2011-07-15

目录

    /

    返回文章
    返回