2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

平稳Tetrolet变换算法研究

张德祥 寻丽娜 刘凯峰 张晶晶 卢一相

张德祥, 寻丽娜, 刘凯峰, 张晶晶, 卢一相. 平稳Tetrolet变换算法研究. 自动化学报, 2018, 44(11): 2041-2055. doi: 10.16383/j.aas.2017.c160827
引用本文: 张德祥, 寻丽娜, 刘凯峰, 张晶晶, 卢一相. 平稳Tetrolet变换算法研究. 自动化学报, 2018, 44(11): 2041-2055. doi: 10.16383/j.aas.2017.c160827
ZHANG De-Xiang, XUN Li-Na, LIU Kai-Feng, ZHANG Jing-Jing, LU Yi-Xiang. Research on Stationary Tetrolet Transform Algorithm. ACTA AUTOMATICA SINICA, 2018, 44(11): 2041-2055. doi: 10.16383/j.aas.2017.c160827
Citation: ZHANG De-Xiang, XUN Li-Na, LIU Kai-Feng, ZHANG Jing-Jing, LU Yi-Xiang. Research on Stationary Tetrolet Transform Algorithm. ACTA AUTOMATICA SINICA, 2018, 44(11): 2041-2055. doi: 10.16383/j.aas.2017.c160827

平稳Tetrolet变换算法研究

doi: 10.16383/j.aas.2017.c160827
基金项目: 

中国博士后科学基金 2015M582826

国家自然科学基金 61402004

国家自然科学基金 61272025

详细信息
    作者简介:

    张德祥  安徽大学电气工程与自动化学院教授.2011年获安徽大学计算机科学与技术学院博士学位.主要研究方向为图像处理, 计算机视觉与模式识别.E-mail:dqxyzdx@126.com

    刘凯峰  安徽大学电气工程与自动化学院讲师.2000年获中国科学院合肥等离子体物理研究所硕士学位.主要研究方向为图像处理与模式识别.E-mail:kaifengliu@126.com

    张晶晶  安徽大学电气工程与自动化学院副教授.2009年获中国科学院合肥物质研究院博士学位.主要研究方向为遥感图像处理, 机器学习和模式识别.E-mail:fannyzjj@sina.com

    卢一相  安徽大学电气工程与自动化学院副教授.2015年获安徽大学电子科学与技术学院博士学位.主要研究方向为小波分析, 图像处理, 统计信号处理, 稀疏表示.E-mail:lyxahu@ahu.edu.cn

    通讯作者:

    寻丽娜  安徽大学电气工程与自动化学院讲师.2008年获中国科学院安徽光学精密机械研究所博士学位.主要研究方向为遥感信息处理, 偏振数据表征与解析.本文通信作者.E-mail:xunlina@126.com

Research on Stationary Tetrolet Transform Algorithm

Funds: 

China Postdoctoral Science Foundation 2015M582826

National Natural Science Foundation of China 61402004

National Natural Science Foundation of China 61272025

More Information
    Author Bio:

     Professor at the College of Electrical Engineering and Automation, Anhui University. He received his Ph. D. degree from the College of Computer Science and Technology, Anhui University in 2011. His research interest covers image processing, computer vision, and pattern recognition

     Lecturer at the College of Electrical Engineering and Automation, Anhui University. He received his master degree from the Institute of Hefei Plasma Physics, Chinese Academy of Sciences in 2000. His research interest covers image processing and pattern recognition

     Associate professor at the College of Electrical Engineering and Automation, Anhui University. She received her Ph. D. degree from the Institute of Hefei Material, Chinese Academy of Sciences in 2009. Her research interest covers remote sensing image processing, machine learning, and pattern recognition

     Associate professor at the College of Electrical Engineering and Automation, Anhui University. He received his Ph. D. degree from the Institute of Electronic Science and Technology, Anhui University in 2015. His research interest covers wavelet analysis, image proce

    Corresponding author: XUN Li-Na  Lecturer at the College of Electrical Engineering and Automation, Anhui University. She received her Ph. D. degree from Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences in 2008. Her research interest covers remote sensing information processing, polarization data characterization and analysis. Corresponding author of this paper
  • 摘要: 为了得到有效的图像多尺度几何表达,提出一种有效的基于Haar小波变换的平稳Tetrolet变换算法.平稳Tetrolet变换是一种由四个单位正方形通过边连接起来的新的自适应Haar类小波变换,对应的滤波器组简单而有效.与标准二维小波变换相比,平稳Tetrolet变换是一种新型基于四格拼板的多尺度几何变换工具,能够通过多方向选择有效地捕获图像中各向异性特性.本文对平稳Tetrolet变换的分解和重构算法进行了详细描述,对利用平稳Tetrolet变换对图像的分解进行了仿真与分析.实验结果表明,与传统算法相比,提出的算法在保留原始图像边缘和纹理信息的同时,可以有效地取得较好的稀疏表达,能消除Tetrolet变换算法对图像融合存在方块效应的缺陷.
    1)  本文责任编委 胡清华
  • 图  1  自由四格拼板的5种基本形式

    Fig.  1  The five fundamental forms of free tetrominoes

    图  2  Tetrolet基变换的22种四格拼板结构

    Fig.  2  kinds of tetrominoes structures for tetrolet basis transformation

    图  3  图像的Tetrolet变换的多尺度分解结构图

    Fig.  3  Image multiscale decomposition structure using tetrolet transforms

    图  4  采用Tetrolet变换不同层次分解的图像融合结果

    Fig.  4  Image fusion results with different levels decomposition using tetrolet transform

    图  5  图像的平稳Tetrolet变换的多尺度分解结构图

    Fig.  5  Image multiscale decomposition structure using stationary tetrolet transform

    图  6  小波变换和Tetrolet变换3层分解系数图像

    Fig.  6  The three layer decomposition coefficient image using wavelet transform and tetrolet transform

    图  7  平稳小波变换和平稳Tetrolet变换3层分解系数图像

    Fig.  7  The three layer decomposition coefficient image using stationary wavelet transform and stationary tetrolet transform

    图  8  采用Tetrolet变换不同层次分解的图像融合结果

    Fig.  8  Image fusion results with different levels of decomposition using tetrolet transform

    图  9  不同分解方法的图像融合结果

    Fig.  9  Image fusion results of different decomposition methods

    图  10  平稳小波和平稳Tetrolet变换分解归一化系数分布图

    Fig.  10  Normalized coefficient distribution map using stationary wavelet transform and stationary tetrolet transform

    图  11  遥感极化图像的水平极化方向图像

    Fig.  11  Horizontally polarized images of remote sensing polarimetric image

    图  12  平稳小波和平稳Tetrolet变换分解归一化系数分布图

    Fig.  12  Normalized coefficient distribution map using stationary wavelet transform and stationary tetrolet transform

    表  1  图 8中不同分解层次的融合图像定量指标

    Table  1  Quantitative index of fusion image using different decomposition levels in Fig. 8

    不同层次分解 均值 标准差 熵值 平均交叉熵 均方根交叉熵
    2层分解 77.419 39.713 6.959 0.3635 0.4455
    4层分解 77.408 39.941 6.974 0.2678 0.3283
    5层分解 77.356 40.114 6.983 0.2318 0.2843
    7层分解 77.287 40.334 6.999 0.2045 0.2509
    下载: 导出CSV

    表  2  不同分解方法的融合图像定量指标

    Table  2  Quantitative index of fusion image using different decomposition methods

    不同分解方法 均值 标准差 熵值 平均交叉熵 均方根交叉熵
    WT 140.273 63.452 7.5993 0.4487 0.4502
    SWT 140.318 63.866 7.5959 0.4440 0.4480
    CT 140.561 63.226 7.6052 0.4302 0.4325
    NSCT 140.356 63.703 7.5910 0.4412 0.4458
    DT 140.411 61.289 7.5268 0.4897 0.4974
    STT 140.309 63.216 7.6236 0.4462 0.4476
    下载: 导出CSV

    表  3  图 9中不同融合结果与图 9(a), (b)的边缘保持指数

    Table  3  Edge preserving index of different fusion results in Fig. 9. with the comparison of graph 9(a) and (b)

    比较对象 WT SWT CT NSCT DT STT
    9(a) 0.9817 0.9447 0.9643 0.9739 0.9903 0.9421
    9(b) 0.9920 0.9547 0.9745 0.9842 1.0008 0.9520
    下载: 导出CSV
  • [1] 陈勇, 樊强, 帅锋.基于小波分析的图像稀疏保真度评价.电子与信息学报, 2015, 37(9):2055-2061 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201509004

    Chen Yong, Fan Qiang, Shuai Feng. Sparse image fidelity evaluation based on wavelet analysis. Journal of Electronics and Information Technology, 2015, 37(9):2055-2061 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201509004
    [2] Meyer F G, Coifman R R. Brushlets:a tool for directional image analysis and image compression. Applied and Computational Harmonic Analysis, 1997, 4(2):147-187 doi: 10.1006/acha.1997.0208
    [3] Candés E J, Donoho D L. Ridgelets:a key to higher-dimensional intermittency? Philosophical Transactions of the Royal Society A:Mathematical, Physical and Engineering Sciences, 1999, 357(1760):2495-2509 doi: 10.1098/rsta.1999.0444
    [4] Starck J L, Candés E J, Donoho D L. The curvelet transform for image denoising. IEEE Transactions on Image Processing, 2002, 11(6):670-684 doi: 10.1109/TIP.2002.1014998
    [5] Donoho D L. Wedgelets:nearly minimax estimation of edges. The Annals of Statistics, 1999, 27(3):859-897 doi: 10.1214/aos/1018031261
    [6] Mallat S, Peyré G. A review of bandlet methods for geometrical image representation. Numerical Algorithms, 2007, 44(3):205-234 doi: 10.1007/s11075-007-9092-4
    [7] Do M N, Vetterli M. The Contourlet transform:an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 2005, 14(12):2091-2106 doi: 10.1109/TIP.2005.859376
    [8] Velisavljević V, Beferull-Lozano B, Vetterli M, Dragotti P L. Directionlets:anisotropic multidirectional representation with separable filtering. IEEE Transactions on Image Processing, 2006, 15(7):1916-1933 doi: 10.1109/TIP.2006.877076
    [9] Velisavljević V, Vetterli M, Beferull-Lozano B, Dragotti P L. Sparse image representation by directionlets. Advances in Imaging and Electron Physics, 2010, 161:147-209 doi: 10.1016/S1076-5670(10)61004-X
    [10] 张德祥, 张晶晶, 吴小培, 高清维.基于Directionlets变换的偏振图像融合.电子与信息学报, 2011, 33(12):2795-2800 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201112001

    Zhang De-Xiang, Zhang Jing-Jing, Wu Xiao-Pei, Gao Qing-Wei. Fusion of polarization image based on directionlets transform. Journal of Electronics and Information Technology, 2011, 33(12):2795-2800 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201112001
    [11] Lu Y X, Gao Q W, Sun D, Xia Y, Zhang D X. SAR speckle reduction using Laplace mixture model and spatial mutual information in the directionlet domain. Neurocomputing, 2015, 173:633-644 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0f3cf356e998c51911fa1bf31045295a
    [12] Krommweh J. Tetrolet transform:a new adaptive haar wavelet algorithm for sparse image representation. Journal of Visual Communication and Image Representation, 2010, 21(4):364-374 doi: 10.1016/j.jvcir.2010.02.011
    [13] Raghuwanshi G, Tyagi V. Texture image retrieval using adaptive tetrolet transforms. Digital Signal Processing, 2016, 48:50-57 doi: 10.1016/j.dsp.2015.09.003
    [14] Jain P, Tyagi V. An adaptive edge-preserving image denoising technique using tetrolet transforms. The Visual Computer, 2015, 31(5):657-674 doi: 10.1007/s00371-014-0993-7
    [15] 陈原, 张荣, 尹东.基于Tetrolet Packet变换的SAR图像稀疏表示.电子与信息学报, 2012, 34(2):261-267 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201202002

    Chen Yuan, Zhang Rong, Yin Dong. SAR image sparse representation based on tetrolet packet transform. Journal of Electronics and Information Technology, 2012, 34(2):261-267 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201202002
    [16] 高继森, 董亚楠, 沈瑜, 张春兰.基于改进Tetrolet变换的图像融合算法研究.计算机科学, 2015, 42(5):320-322 http://d.old.wanfangdata.com.cn/Periodical/jsjkx201505065

    Gao Ji-Sen, Dong Ya-Nan, Shen Yu, Zhang Chun-Lan. Research of image fusion algorithm based on improved tetrolet transform. Computer Science, 2015, 42(5):320-322 http://d.old.wanfangdata.com.cn/Periodical/jsjkx201505065
    [17] 周雨薇, 杨平吕, 陈强, 孙权森.基于MTF和变分的全色与多光谱图像融合模型.自动化学报, 2015, 41(2):342-352 http://www.aas.net.cn/CN/abstract/abstract18613.shtml

    Zhou Yu-Wei, Yang Ping-Lv, Chen Qiang, Sun Quan-Sen. Pan-sharpening model based on MTF and variational method. Acta Automatica Sinica, 2015, 41(2):342-352 http://www.aas.net.cn/CN/abstract/abstract18613.shtml
    [18] Krommweh J, Ma J W. Tetrolet shrinkage with anisotropic total variation minimization for image approximation. Signal Processing, 2010, 90(8):2529-2539 doi: 10.1016/j.sigpro.2010.02.022
    [19] 沈瑜, 伍忠东, 王小鹏, 董亚楠, 江娜.基于模糊算子的Tetrolet变换图像融合算法.计算机科学与探索, 2015, 9(9):1132-1138 http://d.old.wanfangdata.com.cn/Periodical/jsjkxyts201509014

    Shen Yu, Wu Zhong-Dong, Wang Xiao-Peng, Dong Ya-Nan, Jiang Na. Tetrolet transform images fusion algorithm based on fuzzy operator. Journal of Frontiers of Computer Science and Technology, 2015, 9(9):1132-1138 http://d.old.wanfangdata.com.cn/Periodical/jsjkxyts201509014
    [20] 闫莉萍, 刘宝生, 周东华.一种新的图像融合及性能评价方法.系统工程与电子技术, 2007, 29(4):509-513 doi: 10.3321/j.issn:1001-506X.2007.04.003

    Yan Li-Ping, Liu Bao-Sheng, Zhou Dong-Hua. Novel image fusion algorithm with novel performance evaluation method. Systems Engineering and Electronics, 2007, 29(4):509-513 doi: 10.3321/j.issn:1001-506X.2007.04.003
    [21] 王志明.无参考图像质量评价综述.自动化学报, 2015, 41(6):1062-1079 http://www.aas.net.cn/CN/abstract/abstract18682.shtml

    Wang Zhi-Ming. Review of no-reference image quality assessment. Acta Automatica Sinica, 2015, 41(6):1062-1079 http://www.aas.net.cn/CN/abstract/abstract18682.shtml
    [22] 张小利, 李雄飞, 李军.融合图像质量评价指标的相关性分析及性能评估.自动化学报, 2014, 40(2):306-315 http://www.aas.net.cn/CN/abstract/abstract18292.shtml

    Zhang Xiao-Li, Li Xiong-Fei, Li Jun. Validation and correlation analysis of metrics for evaluating performance of image fusion. Acta Automatica Sinica, 2014, 40(2):306-315 http://www.aas.net.cn/CN/abstract/abstract18292.shtml
  • 加载中
图(12) / 表(3)
计量
  • 文章访问数:  1550
  • HTML全文浏览量:  211
  • PDF下载量:  509
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-12-16
  • 录用日期:  2017-10-10
  • 刊出日期:  2018-11-20

目录

    /

    返回文章
    返回