Han Ze, Lin Suzhen. Multiband fusion image evaluation method based on correlation between subject and object evaluation[J]. Opto-Electronic Engineering, 2017, 44(9): 895-902. doi: 10.3969/j.issn.1003-501X.2017.09.006
Citation: Han Ze, Lin Suzhen. Multiband fusion image evaluation method based on correlation between subject and object evaluation[J]. Opto-Electronic Engineering, 2017, 44(9): 895-902. doi: 10.3969/j.issn.1003-501X.2017.09.006

Multiband fusion image evaluation method based on correlation between subject and object evaluation

    Fund Project:
More Information
  • It is difficult to select an appropriate evaluation index for current image fusion. In order to solve the problem, a synthesis evaluation index is proposed based on the correlation between subjective and objective evaluations. First, a variety of fusion results are evaluated subjectively from the edge of clarity, natural sense, information and comprehensive evaluation, respectively. Secondly, 14 commonly objective indexes are used to evaluate the fusion results. Then, the subjective and objective results are normalized, and the Spearman correlation coefficient is used to analyze the correlation between the four subjective evaluations and each objective evaluation. Finally, according to the correlation, a comprehensive index is constructed through 14 objective indexes in 4 aspects. The experimental results show that the synthesis index is more relevant to subjective evaluation than the individual evaluation index and any other comprehensive indexes.
  • 加载中
  • [1] Li Shutao, Kang Xudong, Fang Leyuan, 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

    CrossRef Google Scholar

    [2] Ding Li, Huang Hua, Zang Yu. Image quality assessment using directional anisotropy structure measurement [J]. IEEE Transactions on Image Processing, 2017, 26(4): 1799–1809. doi: 10.1109/TIP.2017.2665972

    CrossRef Google Scholar

    [3] Krasula L, Le Callet P, Fliegel K, et al. Quality assessment of sharpened images: challenges, methodology, and objective metrics [J]. IEEE Transactions on Image Processing, 2017, 26(3): 1496–1508. doi: 10.1109/TIP.2017.2651374

    CrossRef Google Scholar

    [4] Vega M T, Mocanu D C, Stavrou S, et al. Predictive no-reference assessment of video quality [J]. Signal Processing: Image Communication, 2017, 52: 20–32. doi: 10.1016/j.image.2016.12.001

    CrossRef Google Scholar

    [5] Alaql O Ghazinour K, Lu Cheng Chang. Classification of image distortions for image quality assessment [C]// Proceedings of International Conference on Computational Science and Computational Intelligence. 2016: 653–658.

    Google Scholar

    [6] 颜文, 龚飞, 周颖, 等.基于NSST与自适应PCNN相结合的卫星云图融合[J].光电工程, 2016, 43(10): 70–76, 83. doi: 10.3969/j.issn.1003-501X.2016.10.012

    CrossRef Google Scholar

    Yan Wen, Gong Fei, Zhou Ying, et al. Satellite cloud image fusion based on adaptive PCNN and NSST [J]. Opto-Electronic Engineering, 2016, 43(10): 70–76, 83. doi: 10.3969/j.issn.1003-501X.2016.10.012

    CrossRef Google Scholar

    [7] 殷明, 段普宏, 褚标, 等.结合SIST和压缩感知的CT与MRI图像融合[J].光电工程, 2016, 43(8): 47–52.

    Google Scholar

    Yin Ming, Duan Puhong, Chu Biao, et al. CT and MRI medical image fusion based on shift-invariant shearlet transform and compressed sensing [J]. Opto–Electronic Engineering, 2016, 43(8): 47–52.

    Google Scholar

    [8] 张学典, 汪泓, 江旻珊, 等.显著性分析在对焦图像融合方面的应用[J].光电工程, 2017, 44(4): 435–441.

    Google Scholar

    Zhang Xuedian, Wang Hong, Jiang Minshan, et al. Applications of saliency analysis in focus image fusion [J]. Opto-Electronic Engineering, 2017, 44(4): 435–441.

    Google Scholar

    [9] Liu Yu, Chen Xun, Peng Hu, et al. Multi-focus image fusion with a deep convolutional neural network [J]. Information Fusion, 2017, 36: 191–207. doi: 10.1016/j.inffus.2016.12.001

    CrossRef Google Scholar

    [10] Zhang Kai, Wang Min, Yang Shuyuan. Multispectral and hyperspectral image fusion based on group spectral embedding and low-rank factorization [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1363–1371. doi: 10.1109/TGRS.2016.2623626

    CrossRef Google Scholar

    [11] Wang Zhou, Bovik A C. A universal image quality index [J]. IEEE Signal Processing Letters, 2002, 9(3): 81–84 doi: 10.1109/97.995823

    CrossRef Google Scholar

    [12] He Guiqing, LiangFan, Xing Siyuan, et al. Study on algorithm evaluation of image fusion based on multi-hierarchical synthetic analysis[C]// Proceedings of 2016 IEEE International Conference on Signal Processing, Communications and Computing, 2016: 1–6.https://ieeexplore.ieee.org/document/7753704/

    Google Scholar

    [13] 朱亚辉. 红外与可见光图像融合质量评价方法研究[D]. 西安: 西北工业大学, 2015.

    Google Scholar

    Zhu Yahui. Research on quality evaluation methods of infrared and visible image fusion[D]. Xi'an: Northwestern Polytechnical University, 2015.

    Google Scholar

    [14] Xydeas C S, Petrovic V. Objective image fusion performance measure[J]. Electronics Letters, 2000, 36(4): 308–309. doi: 10.1049/el:20000267

    CrossRef Google Scholar

    [15] Piella G, Heijmans H. A new quality metric for image fusion [C]// Proceedings of 2003 International Conference on Image Pro-cessing, 2003, 2: Ⅲ-173–176.

    Google Scholar

    [16] Nizami I F, Majid M, Khurshid K. Efficient feature selection for blind image quality assessment based on natural scene statistics [C]// Proceedings of 2017 14th International Bhurban Conference on Applied Sciences and Technology, 2017: 318–322.https://ieeexplore.ieee.org/document/7868071/

    Google Scholar

    [17] Ding Yong, Zhao Yang, Zhao Xinyu. Image quality assessment based on multi-feature extraction and synthesis with support vector regression [J]. Signal Processing: Image Communication, 2017, 54: 81–92. doi: 10.1016/j.image.2017.03.001

    CrossRef Google Scholar

    [18] Mukherjee R, Debattista K, Bashford-Rogers T, et al. Objective and subjective evaluation of high dynamic range video com-pression [J]. Signal Processing: Image Communication, 2016, 47: 426–437. doi: 10.1016/j.image.2016.08.001

    CrossRef Google Scholar

    [19] Liu Yu, Liu Shuping, Wang Zengfu. 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

    CrossRef Google Scholar

    [20] Jagalingam P, Hegde A V. A Review of quality metrics for fused image [J]. Aquatic Procedia, 2015, 4: 133–142. doi: 10.1016/j.aqpro.2015.02.019

    CrossRef Google Scholar

    [21] 张小利, 李雄飞, 李军.融合图像质量评价指标的相关性分析及性能评估[J].自动化学报, 2014, 40(2): 306–315.

    Google Scholar

    Zhang Xiaoli, LI Xiongfei, LI Jun. Validation and correlation analysis of metrics for evaluating performance of image fusion[J]. Acta Automatica Sinica, 2014, 40(2): 306–315.

    Google Scholar

    [22] Han Yu, Cai Yunze, Cao Yin, et al. A new image fusion performance metric based on visual information fidelity[J]. Information Fusion, 2013, 14(2): 127–135. doi: 10.1016/j.inffus.2011.08.002

    CrossRef Google Scholar

    [23] Warne R T. Testing Spearman's hypothesis with advanced placement examination data[J]. Intelligence, 2016, 57: 87–95. doi: 10.1016/j.intell.2016.05.002

    CrossRef Google Scholar

  • Abstract: Image fusion is an important branch of multi-sensor information fusion, which is to synthesize several images or sequential detective images about one scene into a more complete and thorough image. At present, this technology has achieved a universal usage in remote sense detection, computer vision, target detection and recognition, etc. However, because of the variances of fusion image type, there is no standard evaluation method. Researchers have to select some appropriate evaluation indicators from a number of objective evaluation indicators by experience. The result is that different studies select different evaluation indicators, and it is hard to compare, which leads to lower persuasion in theory study. The hot issue on nowadays study is to choose relative evaluation indicators according to evaluation targets, and synthesize the chosen evaluation indicators to a comprehensive indicator. Indicator accuracy can be achieved through complementary advantages among indicators. An evaluation method of multiband fusion image is proposed based on the correlation of subjective and objective evaluations. This evaluation method includes the following steps. First, subjectively evaluate a variety of fusion results from four aspects. They are the clarity of edge, natural sense, information quantity and comprehensive evaluation. The evaluation level is divided into five levels:"good", "better", "normal", "poor" and"bad". Secondly, calculate the 14 objective evaluation indicators of the fusion results. Thirdly, normalize the subjective and objective evaluation results. Fourthly, use relative Spearman coefficient to calculate the correlation among each evaluation aspect and the 14 objective evaluation indicators. Fifthly, use the correlation to calculate the occupation weight of each objective evaluation indicator in the comprehensive evaluation indicator. Finally, construct a comprehensive index based on the correlation of the 14 indexes for every objective evaluation.

    The experimental results show that the synthesis indicator based on correlation between subject and object evaluation is more relevant to the objective evaluations than the individual evaluation indicator, CMSVD (complex matrix singular value decomposition) and MSA (multi-hierarchical synthesis analysis). The correlation of clarity of edge, natural sense, information quantity and comprehensive evaluation are 0.634, 0.630, 0.737, and 0.661, respectively. As for different evaluation aspects, the correlations between the objective evaluation and subjective evaluation are different. However, the correlations of AG (average gradient), SF (spatial frequency) and VIFF (visual information fidelity for fusion) are relatively higher than other aspects.

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(5)

Tables(5)

Article Metrics

Article views(6285) PDF downloads(3474) Cited by(0)

Access History

Other Articles By Authors

Article Contents

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint