基于改进对比度的有限离散剪切波图像融合

陈清江, 张彦博, 柴昱洲, 魏冰蔗

陈清江, 张彦博, 柴昱洲, 魏冰蔗. 基于改进对比度的有限离散剪切波图像融合[J]. 应用光学, 2016, 37(2): 221-228. DOI: 10.5768/JAO201637.0102004
引用本文: 陈清江, 张彦博, 柴昱洲, 魏冰蔗. 基于改进对比度的有限离散剪切波图像融合[J]. 应用光学, 2016, 37(2): 221-228. DOI: 10.5768/JAO201637.0102004
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Chen Qingjiang, Zhang Yanbo, Chai Yuzhou, Wei Bingzhe. Image fusion based on improved contrast in finite discrete shearlet domain[J]. Journal of Applied Optics, 2016, 37(2): 221-228. DOI: 10.5768/JAO201637.0102004
Citation: Chen Qingjiang, Zhang Yanbo, Chai Yuzhou, Wei Bingzhe. Image fusion based on improved contrast in finite discrete shearlet domain[J]. Journal of Applied Optics, 2016, 37(2): 221-228. DOI: 10.5768/JAO201637.0102004
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基于改进对比度的有限离散剪切波图像融合

  • 西安建筑科技大学 理学院,陕西 西安 710055

基金项目: 

陕西省自然科学基金项目（2015JM1024，2013JK0568）

详细信息
    作者简介:

    陈清江(1966-)，男，河南信阳人，博士，教授，主要从事小波分析，图像处理与信号处理研究。 E-mail: qjchen66xytu@126.com

    通讯作者:

    张彦博(1989-),男，硕士研究生，主要从事小波分析在图像处理中的应用。

  • 中图分类号: TN911.73;TP391.4

Image fusion based on improved contrast in finite discrete shearlet domain

  • School of Science,Xi’an University of Architecture and Technology, Xi’an 710055,China

摘要

    摘要
  • 摘要: 为了提高多聚焦图像的融合精度，结合有限离散剪切波变换(FDST)良好的局部化特性及平移不变性，提出了一种基于有限离散剪切波变换与改进对比度相结合的图像融合新算法。对经过严格配准后的多聚焦图像进行FDST分解，得到低频子带系数和不同尺度不同方向的高频子带系数；对低频子带系数采用区域平均能量匹配度自适应融合算法，高频子带系数的选取则根据低频与高频系数关联得到的对比度进行融合；应用有限离散剪切波逆变换重构得到融合图像，并对融合结果进行主观视觉和客观评价。通过仿真实验，算法在主观视觉效果上有着明显的优越性。在不同融合算法比较的融合结果中，熵值、互信息量和边缘相似度分别平均提高了1.4%、34.6%和8.0%，各项客观评价指标优于其他算法。
    Abstract: In order to improve the accuracy of multifocus image fusion, combining with good localization and shift invariance of finite discrete shearlet transform(FDST), a new image fusion algorithm based on FDST and improved contrast was proposed. Firstly, the registration multifocus images were decomposed by FDST, and the low frequency subband coefficients and high frequency subband coefficients of different scales and directions were obtained. The fusion principle of low frequency subband coefficients was based on the method of regional average energy matching degree. As for high frequency subband coefficients,the sum of contrast was adopted as the fusion rule, which combined the lowfrequency coefficients with the high frequency coefficients. Finally，low frequency information and high frequency information were reconstructed to image by finite discrete shearlet inverse transform, and both subjective visual evaluation and objective performance assessments of the fusion results were implemented. Simulation results indicate that the proposed algorithm is obviously superior on subjective visual effects. The values of entropy, mutual information quantity and edge similarity increase averagely by 1.4%,3.4% and 0.8%, respectively, compared with other fusion algorithms. It is superior to other fusion algorithms on objective evaluation.
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  • [1]Jing Zhongliang, Xiao Gang, Li Zhenhua. Image fusiontheory and applications[M]. Beijing: Higher Education Press, 2007:13. 敬忠良,肖刚,李振华.图像融合理论与应用[M].北京：高等教育出版社,2007:13. [2]Li S, Kwok J T, Wang Y. Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images[J]. Information Fusion, 2002, 3(1):1723. [3]Wang H H. A new multiwaveletbased approach to image fusion.[J]. Journal of Mathematical Imaging & Vision, 2004, 21（2）:177192. [4]Jiao L C, Tan S. Development and prospect of image multiscale geometric analysis[J]. Acta Electronica Sinica, 2003, 31(S1):19751981. 焦李成, 谭山. 图像的多尺度几何分析:回顾和展望[J]. 电子学报, 2003, 31(增刊1):19751981. [5]Cunha A L, Zhou J P, Dom N. The non sub sampled contour let transform: theory, design, and application[J]. IEEE, 2006, 10:30893101. [6]Hauser S, Steidl G. Convex multiclass segmentation with shearlet regularization[J]. International Journal of Computer Mathematics, 2013, 90(1):6281. [7]Han Xiao, Peng Li. New multifocus image fusion algorithm based on wavelet transform[J]. Computer and Digital Engineering, 2014, 42(9):16281631. 韩潇,彭力.基于小波变换的多聚焦图像融合新算法[J].计算机与数字工程,2014,42(9):16281631. [8]Wang Hongmei, Chen Lihua, Li Yanjun, et al. A new and more effective image fusion algorithm based on salient feature[J]. Journal of Northwestern Polytechnical University, 2010, 28(4):486490. 王红梅,陈励华,李言俊,等.一种基于显著特征的图像融合算法[J].西北工业大学学报,2010,28(4):486490. [9]Yue Zhen, Li Fanming. Polarization image fusion algorithm based on wavelet transform[J]. Journal of Applied Optics, 2014, 35(2):321326. 岳振,李范鸣.一种基于小波变换的红外偏振融合算法[J].应用光学,2014,35(2):321326. [10]Wang Zhenglin. Wavelet image fusion algorithm based on contrast[J]. Laser & Infrared, 2014(9):10421044. 王正林. 基于对比度的小波图像融合算法研究[J].激光与红外,2014(9):10421044. [11]Kong Weiwei, Liu Jianping. Technique for image fusion based on NSST domain improved fast nonclassical RF[J]. Infrared Physics & Technology, 2013, 61(6):2736. [12]Pu Tian, Fang Qingzhe, Ni Guoqiang. Contrastbased multiresolution image fusion[J]. Acta Electronica Sinica, 2000(12):116118. 蒲恬,方庆喆,倪国强. 基于对比度的多分辨率图像融合[J].电子学报,2000(12):116118. [13]Yan Lele, Li Hui, Qiu Juneng, et al. Image quality assessment method based on regional contrast and structural similarity[J]. Journal of Applied Optics, 2015,36(1):5863. 闫乐乐,李辉,邱聚能,等.基于区域对比度和SSIM的图像质量评价方法[J].应用光学,2015,36(1):5863. [14]Tong Tao, Yang Guang, Tan Haifeng, et al. Multisensor image fusion algorithm based on NSCT[J]. Geography and GeoInformation Science, 2013, 29(2): 2225,59. 童涛, 杨桄, 谭海峰,等. 基于NSCT变换的多传感器图像融合算法[J]. 地理与地理信息科学, 2013, 29(2)：2225,59.
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出版历程
  • 收稿日期:  2015-08-10
  • 修回日期:  2015-10-10
  • 刊出日期:  2016-03-14

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