计算机科学

计算机科学 ›› 2017, Vol. 44 ›› Issue (3): 318-322.doi: 10.11896/j.issn.1002-137X.2017.03.064

• 图形图像与模式识别 • 上一篇    

基于NSST域的自适应区域和SCM相结合的多聚焦图像融合

赵杰,温馨,刘帅奇,张宇   

  1. 河北大学电子信息工程学院 保定071000 河北省数字医疗工程重点实验室 保定071000,河北大学电子信息工程学院 保定071000 河北省数字医疗工程重点实验室 保定071000,河北大学电子信息工程学院 保定071000 河北省数字医疗工程重点实验室 保定071000,河北大学电子信息工程学院 保定071000 河北省数字医疗工程重点实验室 保定071000
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然科学基金(61572063,61401308),河北大学自然科学研究计划项目(2014-303),河北大学研究生创新资助

Multi-focus Image Fusion Using Adaptive Region and SCM Based on NSST Domain

ZHAO Jie, WEN Xin, LIU Suai-qi and ZHANG Yu   

  • Online:2018-11-13 Published:2018-11-13

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摘要： 为了提高多聚焦图像的融合效果,结合多源图像之间的共享相似性,提出了一种基于非下采样Shearlet变换(Nonsubsampled Shearlet Transform,NSST)域的自适应区域与脉冲发放皮层模型(Spiking Cortical Model,SCM)结合的新型图像融合算法。首先用NSST分解源图像,然后计算边缘能量(Energy Of Edge,EOE),在自适应区域用投票加权法融合低频系数,高频系数由边缘能量作为输入的SCM点火图融合,最后通过逆NSST获得该融合图像。该算法既可以很好地保持源图像的信息,又可以抑制在变换域因非线性运算产生的像素失真。实验结果表明,该方法优于最新的变换域和脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)融合方法。

关键词: 图像融合,NSST,共享相似性,自适应区域,SCM,EOE

Abstract: In order to improve the fusion effect of multi-focus image,combined with the shared similarity among multiple source images,a new image fusion algorithm based on adaptive regions and spiking cortical model (SCM) of nonsubsampled shearlet transform (NSST) domain was proposed.First,NSST is utilized for decomposition of the source images.Then by calculating the energy of edge(EOE),the low frequency coefficients are fused by weight votes in adaptive regions.And the high frequency coefficients is fused by fired map of SCM which is motivated by EOE.Finally the fusion image is gained by inverse NSST.The algorithm can both preserve the information of the source images well and suppress pixel distortion due to nonlinear operations in transform domain.Experimental results demonstrate that the proposed method outperforms the state-of-the-art transform domain and pulse coupled neural network (PCNN) fusion methods.

Key words: Image fusion,NSST,Shared similarity,Adaptive region,SCM,EOE

[1] ZHANG Y X,CHEN L,ZHAO Z H,et al.Multi-focus image fusion with robust principal component analysis and pulse coupled neural network[J].Optik,2014,125(17):5002-5006.
[2] LIU Y,WANG Z F.Simultaneous image fusion and denoising with adaptive sparse Representation[J].IET Image Processing,2015,9(5):347-357.
[3] HUANG W,JING Z L.Evaluation of focus measures in multi-focus image fusion[J].Pattern Recognition Letters,2007,28(4):493-500.
[4] JIA Y H.Fusion of Landsat TM and SAR images based on principal component analysis[J].Remote Sensing Technology and Application,1998,13(1):46-49.
[5] WAN T,ZHU C C,QIN Z C.Multifocus image fusion based on robust principal component analysis[J].Pattern Recognition Letters,2013,34(9):1001-1008.
[6] LI S T,KANG X D,HU J W.Image fusion with guided filtering[J].IEEE Transactions on Image Processing,2013,22(7):2864-2875.
[7] MIAO Q G,WANG B S.Novel Algorithm of Multi-sensor Image Fusion Using Contourlet[J].Computer Science,2008,35(5):231-235.(in Chinese) 苗启广,王宝树.基于Contourlet的图像融合新方法[J].计算机科学,2008,5(5):231-235.
[8] QU X B,YAN J W,XIAO H Z,et al.Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain[J].Acta Automatica Sinica,2008,34(12):1508-1514.
[9] GENG P,WANG Z Y,ZHANG Z G,et al.Image fusion by pulse couple neural network with shearlet[J].Optical Enginee-ring,2012,51(6):067005-1-067005-7.
[10] YANG Y C,WANG X M,DANG J W,et al.Method of medical image fusion based on nonsubsampled Contourlet transform[J].Computer Science,2013,0(3):310-313.(in Chinese) 杨艳春,王晓明,党建武,等.基于非下采样Contourlet变换的医学图像融合方法[J].计算机科学,2013,0(3):310-313.
[11] LIU S Q,HU S H,XIAO Y,et al.SAR Image Edge Detection Based on Local Hybrid Filter[J].Journal of Electronics & Information Technology,2013,35(5):8-18.(in Chinese) 刘帅奇,胡绍海,肖扬,等.基于局部混合滤波的SAR图像边缘检测[J].电子与信息学报,2013,35(5):8-18.
[12] LIU S Q,HU S H,XIAO Y.Image separation using wavelet-complex shearlet dictionary[J].Journal of Systems Engineering and Electronics,2014,25(2):314-321.
[13] LIU S Q,HU S H,XIAO Y,et al.Bayesian shearlet shrinkage for SAR image denoising via sparse representation[J].Multidimensional Systems and Signal Processing,2014,25(4):683-701.
[14] EASLEY G,LABATE D,LIM W Q.Sparse directional image representation using the discrete shearlets transform[J].Applied and Computational Harmonic Analysis,2008 25(1):25-46.
[15] GUO D,YAN J W,QU X B.High quality multi-focus image fusion using self-similarity and depth information[J].Optics Communications,2015,338(1):138-144.
[16] KONG W W,WANG B H,LEI Y.Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model[J].Infrared Physics & Technology,2015,71:87-98.
[17] WANG N Y,MA Y D,ZHAN K.Spiking cortical model for multifocus image fusion[J].Neurocomputing,2014,130(3):44-51.
[18] WANG J J,LI Q,JIA Z H,et al.A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain[J].Optik,2015,126(20):2508-2511.
[19] KUTYNIOK G,LIM W Q.Compactly supported shearlets are optimally sparse[J].Journal of Approximation Theory,2011,163(11):1564-1589.
[20] MA Y D,LIN D M,ZHANG B D,et al.A novel algorithm of image enhancement based on pulse coupled neural network time matrix and rough set[J].International Conference on Fuzzy Systems & Knowledge Discovery,2007,3:86-90.
[21] LIU S Q,ZHU Z H,LI H Y,et al.Multi-focus image fusionusing self-similarity and depth information in nonsubsampled shearlet transform domain[J].International Journal of Signal Processing,Image Processing and Pattern Recognition,2016,9(1):347-360.
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