Please wait a minute...
浙江大学学报(理学版)  2018, Vol. 45 Issue (4): 416-419,426    DOI: 10.3785/j.issn.1008-9497.2018.04.007
数学与计算机科学     
一种基于特征分解的图像融合方法
常莉红
宁夏师范学院 数学与信息科学学院, 宁夏 固原 756000
An image fusion method based on feature decomposition.
CHANG Lihong
School of Mathematics and Computer Science, Ningxia Normal University, Guyuan 756000, Ningxia Province, China
 全文: PDF(7127 KB)   HTML  
摘要: 一幅图像可以分解成几何特征不同的纹理部分和卡通部分,基于这两大特征提出了一种图像融合方法.利用卡通和纹理特征的差异,通过学习分别得到卡通字典和纹理字典.在融合过程中,分别利用特定的卡通和纹理字典对源图像的卡通和纹理部分进行融合,融合后的卡通和纹理部分经简单相加得到融合图像.实验结果表明,所提方法是有效的.
关键词: 图像融合特征分解稀疏表示    
Abstract: An image can be decomposed into texture part and cartoon part adhering to different geometric features. An image fusion method based on these two features is proposed in this paper. A cartoon dictionary and a texture dictionary are learned from the sample images based on their different cartoon and texture features. In the fusion process, the cartoon and texture parts of the source image are fused using the specific cartoon and texture dictionary respectively. Experimental results show that the proposed method is very effective.
Key words: image fusion    feature decomposition    sparse representation
收稿日期: 2017-06-30 出版日期: 2018-07-12
CLC:  TP391  
基金资助: 国家自然科学基金资助项目(61101208);陕西省教育厅专项(16JK1603);重庆文理学院重点实验室开放课题项目(KFJJ1506).
作者简介: 常莉红(1980-),ORCID:http://orcid.org/0000-0002-0399-0551,女,博士,讲师,主要从事稀疏表示和变分方法在图像处理中的应用研究,E-mail:changlihong-1999@126.com.
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
常莉红

引用本文:

常莉红. 一种基于特征分解的图像融合方法[J]. 浙江大学学报(理学版), 2018, 45(4): 416-419,426.

CHANG Lihong. An image fusion method based on feature decomposition.. Journal of Zhejiang University (Science Edition), 2018, 45(4): 416-419,426.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2018.04.007        https://www.zjujournals.com/sci/CN/Y2018/V45/I4/416

[1] LIU Z, FORSYTH D S, SAFIZADEH M S,et al. A data-fusion scheme for quantitative image analysis by using locally weighted regression and Dempster-Shafer theory[J]. IEEE Transactions on Instrumentation and Measurement,2008,57(11):2554-2560.
[2] HASSAINIA F, MAGAFIA I, LANGEVIN F. Image fusion by an orthogonal wavelet transform and comparison with other methods[C]//International Conference of the IEEE Engineering in Medicine and Biology Society. Paris:The IEEE Engineering in Medicine and Biology Society, 1992(3):1246-1247.
[3] PETROVIC V S, XYDEAS C S. Gradient-based multiresolution image fusion[J]. IEEE Transactions on Image Processing, 2004, 13(2):228-237.
[4] LI H, MANJUNATH B, MITRA S. Multisensor image fusion using the wavelet transform[J].Graphical Models and Image Processing, 1995, 57(3):235-245.
[5] HILL P, CANAGARAJAH N C, BULL D R. Image fusion using complex wavelet[C]//Proc of the 13th British Machine Vision Conference. Durham:British Machine Vision Association, 2002:487-496.
[6] 常莉红. 一种基于四元数小波变换的图像融合方法[J]. 宝鸡文理学院学报(自然科学版), 2016, 36(3):8-14. CHANG L H. An image fusion method based on quaternion wavelet transform[J].Journal of Baoji University of Art and Sciences(Natural Science Edition), 2016, 36(3):8-14.
[7] YANG B, LI S T. Multifocus image fusion and restoration with sparse representation[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(4):884-892.
[8] LIU Y,WANG Z F. Simultaneous image fusion and denoising with adaptive sparse representation[J]. IET Image Processing, 2014, 9(5):347-357.
[9] LIU Y, LIU S P, WANG Z F. A general framework for image fusion based on multi-scale transform and sparse representation[J].Information Fusion, 2015, 24:147-164.
[10] YIN H T, LI S T, FANG L Y. Simultaneous image fusion and super-resolution using sparse representation[J]. Information Fusion, 2013, 14(3):229-240.
[11] BUADES A, LE T, MOREL J, et al. Cartoon +Texture image decomposition[J].Image Processing on Line, 2011(1):200-207.
[12] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
[13] HOSSNY M, NAHAVANDI S, CREIGHTON D. Comments on ‘Information measure for performance of image fusion’[J]. Electronics Letters,2008, 44(18):1066-1067.
[14] XYDEAS C S, PETROVIÉ V. Objective image fusion performance measure[J]. Electronics Letters, 2000, 36(4):308-309.
[1] 周国华, 蒋晖, 顾晓清, 殷新春. 基于半监督子空间迁移的稀疏表示遥感图像场景分类方法[J]. 浙江大学学报(理学版), 2021, 48(6): 684-693.