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基于Hess矩阵的多聚焦图像融合方法

肖斌 唐翰 徐韵秋 李伟生

肖斌, 唐翰, 徐韵秋, 李伟生. 基于Hess矩阵的多聚焦图像融合方法[J]. 电子与信息学报, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497
引用本文: 肖斌, 唐翰, 徐韵秋, 李伟生. 基于Hess矩阵的多聚焦图像融合方法[J]. 电子与信息学报, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497
XIAO Bin, TANG Han, XU Yunqiu, LI Weisheng. Multi-focus Image Fusion Based on Hess Matrix[J]. Journal of Electronics & Information Technology, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497
Citation: XIAO Bin, TANG Han, XU Yunqiu, LI Weisheng. Multi-focus Image Fusion Based on Hess Matrix[J]. Journal of Electronics & Information Technology, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497

基于Hess矩阵的多聚焦图像融合方法

doi: 10.11999/JEIT170497
基金项目: 

国家自然科学基金(61572092, U1401252),国家重点研发计划(2016YFC1000307-3)

Multi-focus Image Fusion Based on Hess Matrix

Funds: 

The National Natural Science Foundation of China (61572092, U1401252), The National Science and Technology Major Project (2016YFC1000307-3)

  • 摘要: 该文提出了一种基于Hess矩阵的多聚焦图像融合方法。该方法利用多尺度下的Hess矩阵检测特征和背景区域,并在此基础上,将源图像分成特征区域与非特征区域,分别采用不同的融合策略生成决策图;然后通过结合不同部分的决策图,得到初始决策图;最后采用后处理方法对初始决策图进行精化,得到最终的融合图像。为了提高融合效果,该文还提出了一种基于多尺度Hess矩阵的聚焦评价方法。同时引入积分图像进行快速计算,以满足实时性要求。实验结果表明,该方法在主观视觉感知和客观评价指标方面都要略优于现有的方法。
  • LI H, LI X, YU Z, et al. Multifocus image fusion by combining with mixed-order structure tensors and multiscale neighborhood[J]. Information Sciences An International Journal, 2016, s349(C): 25-49. doi: 10.1016/j.ins.2016.02.030.
    BAI X, ZHANG Y, ZHOU F, et al. Quadtree-based multi- focus image fusion using a weighted focus-measure[J]. Information Fusion, 2015, 22: 105-118. doi: 10.1016/j.inffus. 2014.05.003.
    PETROVIC V S and XYDEAS C S. Gradient-based multiresolution image fusion[J]. IEEE Transactions on Image Processing, 2004, 13(2): 228-237. doi: 10.1109/TIP.2004. 823821.
    LEWIS J J, OCALLAGHAN R J, NIKOLOV S G, et al. Pixel-and region-based image fusion with complex wavelets[J]. Information Fusion, 2007, 8(2): 119-130. doi: 10.1016/j.inffus. 2005.09.006.
    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.
    LIU Y, LIU S, and WANG Z. Multi-focus image fusion with dense SIFT[J]. Information Fusion, 2015, 23(C): 139-155. doi: 10.1016/j.inffus.2014.05.004.
    LI S, KANG X, and HU J. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2864-2875. doi: 10.1109/TIP.2013.2244222.
    LI S, KANG X, HU J, et al. Image matting for fusion of multi-focus images in dynamic scenes[J]. Information Fusion, 2013, 14(2): 147-162. doi: 10.1016/j.inffus.2011.07.001.
    ZHOU Z, LI S, and WANG B. Multi-scale weighted gradient- based fusion for multi-focus images[J]. Information Fusion, 2014, 20(1): 60-72. doi: 10.1016/j.inffus.2013.11.005.
    WANG Z, MA Y, and GU J. Multi-focus image fusion using PCNN[J]. Pattern Recognition, 2010, 43(6): 2003-2016. doi: 10.1016/j.patcog.2010.01.011.
    LOWE and DAVID G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
    BAY H, ESS A, and TUYTELAARS T. Speeded-up robust features[J]. Computer Vision Image Understanding, 2008, 110(3): 404-417.
    ZHANG Y, BAI X, and WANG T. Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure[J]. Information Fusion, 2017, 35: 81-101. doi: 10.1016/j.inffus.2016.09.006.
    VIOLA P and JONES M. Rapid object detection using a boosted cascade of simple features[C]. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii, 2001, (I): 511-518.
    ZHANG Q and LEVINE M D. Robust multi-focus image fusion using multi-task sparse representation and spatial context[J]. IEEE Transactions on Image Processing, 2016, 25(5): 2045-2058. doi: 10.1109/TIP.2016.2524212.
    ZHANG B, LU X, PEI H, et al. Multi-focus image fusion algorithm based on focused region extraction[J]. Neurocomputing, 2016, 174(PB): 733-748. doi: 10.1016/j.ins. 2016.02.030.
    LIU Y, CHEN X, PENG H, 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.
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出版历程
  • 收稿日期:  2017-05-24
  • 修回日期:  2017-10-18
  • 刊出日期:  2018-02-19

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