基于NSCT变换和相似信息鲁棒主成分分析模型的图像融合技术

doi:10.13229/j.cnki.jdxbgxb20170928

摘要/Abstract

摘要： 针对传统的图像处理是以单个像素点为基础进行融合而忽略了信息的相似性以及存在信息丢失的问题,提出了基于非下采样Contourlet变换(Nonsubsampled contourlet transform, NSCT)和相似信息鲁棒主成分分析(Robust principle component analysis, RPCA)模型的图像融合技术。首先对源图像获取图像块构造初始矩阵,通过对构造矩阵进行NSCT分解获得高频和低频部分,利用提出的具有相似信息低秩矩阵模型将低频成分分解成低秩矩阵和稀疏误差矩阵,再分别对两幅图像的低秩矩阵、稀疏误差矩阵及高频成分采用绝对值最大法融合规则进行融合,最后通过逆变换得到融合图像。MRI和CT的脑部图像的实验分析结果表明,本文算法可以更好地保留源图像中的边缘和纹理信息。

关键词: 图像处理, 图像融合, 非下采样Contourlet变换, 鲁棒主成分分析, 低秩矩阵

Abstract: In traditional medical image fusion process based on single pixel, the information similarity is ignored and detailed information may loss. To solve these problems, an image fusion technology based on Nonsubsampled Contourlet (NSCT) and Robust Principal Component Analysis (RPCA) model with similar information is proposed. First, the initial matrix constructed by the image block from the original images is decomposed into low frequency and high frequency parts by NSCT transformation. Second, the low rank component is decomposed into low rank matrix and spare error matrix by using the low rank matrix model with similar information. Third, the low rank matrix of the two images, the spare error matrix and high-frequency components are fused by fusion rule of the absolute maximum method. Finally the fusion image is replaced by the inverse transform. The experiment results on CT and MRI show that the proposed method can maintain more edge and texture detailed information of the source images.

Key words: image processing, image fusion, Nonsubsampled contourlet transform(NSCT), robust principal component analysis, low-rank matrix

中图分类号:

TP391.4

刘哲, 徐涛, 宋余庆, 徐春艳. 基于NSCT变换和相似信息鲁棒主成分分析模型的图像融合技术[J]. 吉林大学学报(工学版), 2018, 48(5): 1614-1620.

LIU Zhe, XU Tao, SONG Yu-qing, XU Chun-yan. Image fusion technology based on NSCT and robust principal component analysis model with similar information[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1614-1620.

参考文献

[1] Du J, Li W, Lu K, et al.An overview of multi-modal medical image fusion[J]. Neurocomputing, 2016, 215:3-20.
[2] Yin H.Sparse representation with learned multiscale dictionary for image fusion[J]. Neurocomputing, 2015, 148:600-610.
[3] Yin H, Li Y, Chai Y, et al.A novel sparse-representation-based multi-focus image fusion approach[J]. Neurocomputing, 2016, 216:216-229.
[4] Liu Y, Liu S, Wang Z.A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24:147-164.
[5] Ren K, Xu F.Super-resolution images fusion via compressed sensing and low-rank matrix decomposition[J]. Infrared Physics and Technology, 2015, 68:61-68.
[6] Wang Q, Li S, Qin H, et al.Robust multi-modal medical image fusion via anisotropic heat diffusion guided low-rank structural analysis[J]. Information Fusion, 2015, 26(C):103-121.
[7] Candès E J,Li X,Ma Y,et al.Robust principal component analysis[J].Journal of the ACM,2011,58(3):11.
[8] Ni Y, Sun J, Yuan X, et al.Robust low-rank subspace segmentation with semidefinite guarantees[C]∥IEEE International Conference on Data Mining Workshops,IEEE,Computer Society,2010:1179-1188.
[9] Liu G, Lin Z, Yan S, et al.Robust recovery of subspace structures by low-rank representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(1):171-184.
[10] Candès,Emmanuel J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus Mathematique,2008,346(9/10):589-592.
[11] 史加荣, 郑秀云, 魏宗田,等. 低秩矩阵恢复算法综述[J]. 计算机应用研究, 2013, 30(6):1601-1605.
Shi Jia-rong,Zheng Xiu-yun, Wei Zong-tian,et al.Survey on algorighms of low-rank matrix recovery[J].Application Research of Computers,2013, 30(6):1601-1605.
[12] 刘园园. 快速低秩矩阵与张量恢复的算法研究[D]. 西安:西安电子科技大学智能感知与图像理解教育部重点实验室, 2013.
Liu Yuan-yuan.Algorithm research of fast low-rand matrix and tenser recovery[D].Xi'an: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University,2013.
[13] Chandrasekaran V, Sanghavi S, Parrilo P A, et al.Rank-sparsity incoherence for matrix decomposition[J]. SIAM Journal on Optimization, 2011, 21(2):572-596.
[14] Cai J F,Cand S E J,et al.A singular value thresholding algorithm for matrix completion[J]. SIAM Journal on Optimization,2010,20(4):1956-1982.
[15] Cunha A L D, Zhou J, Do M N.The nonsubsampled contourlet transform:Theory Desigh,and Applications[J].IEEE Transactions on Image Processing,2006,15(10):3089-3100.
[16] 崔玲玲, 卢朝阳, 李静,等. 基于非下采样Contourlet域高斯混合模型的布匹瑕疵识别算法[J]. 吉林大学学报:工学版, 2013, 43(3):734-739.
Cui Ling-ling, Lu Zhao-yang,Li Jing, et al.Fabric defect recognition algorithm based on Gaussian mixture model in nonsubsampled contourlet domain[J].Journal of Jilin University(Engineering and Technology Edition),2013, 43(3):734-739.
[17] http://www.med.harvard.edu/AANLIB/.[2017-07-08].
[18] Kumar B K S. Image fusion based on pixel significance using cross bilateral filte[J]. Signal, Image and Video Processing, 2015, 9(5):1193-1204.
[19] Xydeas C S, Petrovic V.Objective image fusion performance measure[J]. Military Technical Courier, 2000, 36(4):308-309.
[20] 郭雷,李晖晖,鲍永生. 图像融合[M]. 北京:电子工业出版社,2008.
[21] Prakash O, Srivastava R, Khare A.Biorthogonal wavelet transform based image fusion using absolute maximum fusion rule[C]∥Information and Communication Technologies,IEEE,2013:577-582.
[22] Jiao Z, Shao J, Xu B.A Novel Multi-focus Image Fusion Method Using NSCT and PCNN[M].Berlin Heidelberg:Springer,2012:161-170.
[23] Niu Y, Shen L.The optimal multi-objective optimization using PSO in blind color image fusion[C]∥International Conference on Multimedia & Ubiquitous Engineering,IEEE Computer Society, 2007:970-975.
[24] 刘羽, 汪增福. 结合小波变换和自适应分块的多聚焦图像快速融合[J].中国图象图形学报, 2013, 18(11):1435-1444.
Liu Yu, Wang Zeng-fu.Multi-focus image fusion based on wavelet transform and adaptive block[J].Journal of Image and Graphics.2013, 18(11):1435-1444.
[25] Li S, Kang X, Hu J.Image fusion with guided filtering[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2013, 22(7):2864-2875.

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