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显著性分析在对焦图像融合方面的应用

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文章导航 > 光电工程 > 2017 Vol. 44 > No. 4 > 435-441

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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. doi: 10.3969/j.issn.1003-501X.2017.04.008
引用本文: 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. doi: 10.3969/j.issn.1003-501X.2017.04.008
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Xuedian Zhang, Hong Wang, Minshan Jiang, et al. Applications of saliency analysis in focus image fusion[J]. Opto-Electronic Engineering, 2017, 44(4): 435-441. doi: 10.3969/j.issn.1003-501X.2017.04.008
Citation: Xuedian Zhang, Hong Wang, Minshan Jiang, et al. Applications of saliency analysis in focus image fusion[J]. Opto-Electronic Engineering, 2017, 44(4): 435-441. doi: 10.3969/j.issn.1003-501X.2017.04.008
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显著性分析在对焦图像融合方面的应用

    • 上海理工大学光电信息与计算机工程学院，上海 200093

  • 基金项目:
    国家重大仪器专项(2014YQ09070903、2013YQ03065104)；国家高技术研究发展计划(2015AA0200751)
详细信息

Applications of saliency analysis in focus image fusion

    • Institute of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

  • Fund Project:
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    摘要

  • 针对自动对焦技术中存在的全局对焦困难问题，本文提出一种新的基于区域显著性分析的图像融合方法。首先用基于图论的显著性分析(GBVS)算法定位源图像中的聚焦区域，然后使用分水岭和形态学方法进一步处理显著图的封闭区域以去除伪聚焦区域，得到精确提取的聚焦区域；离焦区域用剪切波变换处理后，以SML算子选取有用的细节信息作为融合依据。最后将处理后的聚焦区域和离焦区域融合为全聚焦图像。实验证明，所提出的方法融合图像边缘清晰，细节丰富，视觉效果最好，并且在清晰度和融合度的评价指标上较传统方法提高5%以上。

    In the study of autofocus technology, we propose an image fusion method based on saliency analysis, which can solve the problem of all in focus. First, the focal area in the source image is positioned by the graph-based visual saliency (GBVS) algorithm, and then the watershed and morphological methods are used to obtain the closed area of the saliency graph and the pseudo-focus region is removed. The defocused region is processed by the Shearlet transform, and the SML operator is used to choose the fusion parts. Finally, the precise focused region and the processed defocused region are fused into an all in focus image. Experiments show that the fused image of our method is clear and rich in detail, which has the best visual effect, and improves more than 5% in definition and fusion compared with traditional methods.

    • Overview

  • Abstract: In the study of autofocus technology, it is always difficult to obtain the all-in focus images because of optical system's limited focus depth, but a high definition image is very important to scientific research. In the research of autofocus technology, we know that detection of the focused region is the key issue of the multi-focus image fusion algorithm, the blurred boundary of the focused region increases the more difficulty of identifying focused regions accurately. According to these principles, the focused region of the source image should be directly fused as much as possible. We propose an image fusion method based on saliency analysis, which can solve the problem of all-in focus. The saliency analysis simulates the human visual attention mechanism by calculating color, direction, brightness and other characteristic information to get the visual significance of the image. The focus of human eyes usually falls in the region of higher saliency. With the saliency analysis, the saliency maps are obtained by comparing the differences among the components of the characteristics which are used to identify the focused regions of the multi-focus image. The process of our method can be described as follow: First, the focal area in the source image is positioned by the graph-based visual saliency (GBVS) algorithm, and then the watershed and morphological methods are used to obtain the closed area of the saliency region and the pseudo-focus regions are removed. In consideration of the defocused region containing much texture and direction information, the defocused region is processed by the Shearlet transform, and the SML operator is used to choose the fusion parts. Finally, the precisely focused region and the processed defocused region are fused into an all-in focus image. Experiments visually show that the fused image of the proposed method has clear and rich details. Compared with 3 traditional methods in 4 objective evaluation criteria: entropy, QAB/F, MI and SF, NSCT method performs best in the 3 traditional methods, while the entropy of the proposed method is 1% higher than that of NSCT method, QAB/F is about 4% higher, MI and SF are about 2% higher each, which means the proposed method has the clearest image and the best fusion performance. In the broken line graph, the merit of the proposed method is more obvious. The subjective visual effects and objective evaluation of the results demonstrate neatly that the proposed method is an effective image fusion method, and we will do further research on color image fusion.

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  • 图 1  基于显著性分析的全局对焦方法流程图.

    Figure 1.  The flow chart of autofocus method based on saliency analysis.

    下载: 全尺寸图片 幻灯片

    图 2  实验原图. (a1) Clock右对焦. (a2) Clock左对焦. (b1) Pepsi右对焦. (b2) Pepsi左对焦. (c1) Lab右对焦. (c2) Lab左对焦. (d1) Pen右对焦. (d2) Pen左对焦.

    Figure 2.  The source image of the experiment. (a1) Clock right focus. (a2) Clock left focus. (b1) Pepsi right focus. (b2) Pepsi left focus. (c1) Lab right focus. (c2) Lab left focus. (d1) Pen right focus. (d2) Pen left focus.

    下载: 全尺寸图片 幻灯片

    图 3  四种融合方法的融合结果. (a1)~(a4) LAP. (b1)~(b4) Wavelet. (c1)~(c4) NSCT+ PCNN. (d1) ~(d4) GBVS.

    Figure 3.  The results of 4 fusion methods. (a1)~(a4) LAP. (b1)~(b4) Wavelet. (c1)~(c4) NSCT+PCNN. (d1)~(d4) GBVS.

    下载: 全尺寸图片 幻灯片

    图 4  图像融合质量评价对比. (a) E. (b) QAB/F. (c) MI. (d) SF.

    Figure 4.  Comparison of image fusion quality evaluation. (a) E. (b) QAB/F. (c) MI. (d) SF.

    下载: 全尺寸图片 幻灯片

    表 1  图像融合质量评价.

    Table 1.  Image fusion quality evaluation.

    图像 实验方法 评价指标
    E QAB/F MI SF
    Clock LAP 2.8318 0.5899 6.9354 13.0092
    wavelet 2.8972 0.6837 6.9995 13.6347
    NSCT 2.9748 0.6849 7.0747 13.9053
    GBVS 2.9223 0.7168 7.2397 14.2661
    Pepsi LAP 2.8516 0.6477 6.8282 13.1893
    wavelet 2.9811 0.7695 6.8663 13.7388
    NSCT 2.9792 0.7673 6.9228 13.9192
    GBVS 2.9835 0.7826 7.0925 14.1657
    Lab LAP 2.8388 0.5947 7.0148 13.0805
    wavelet 2.8726 0.7014 7.0473 13.8231
    NSCT 2.9517 0.7002 7.1846 14.0397
    GBVS 2.9892 0.7301 7.1435 14.3055
    Pen LAP 2.8201 0.6845 7.0198 13.6592
    wavelet 2.8593 0.7113 7.0285 13.9003
    NSCT 2.9461 0.7164 7.0302 14.2635
    GBVS 2.9904 0.7901 7.0993 14.5338
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收稿日期:  2017-02-06
修回日期:  2017-03-10
刊出日期:  2017-04-15

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