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

Sobolev 广义度量下的各向异性扩散模型

刘孝艳 冯象初 赵晨萍

刘孝艳, 冯象初, 赵晨萍. Sobolev 广义度量下的各向异性扩散模型. 自动化学报, 2015, 41(2): 320-329. doi: 10.16383/j.aas.2015.c140564
引用本文: 刘孝艳, 冯象初, 赵晨萍. Sobolev 广义度量下的各向异性扩散模型. 自动化学报, 2015, 41(2): 320-329. doi: 10.16383/j.aas.2015.c140564
LIU Xiao-Yan, FENG Xiang-Chu, ZHAO Chen-Ping. Anisotropic Diffusion Model Based on Generalized Metric in Sobolev Space. ACTA AUTOMATICA SINICA, 2015, 41(2): 320-329. doi: 10.16383/j.aas.2015.c140564
Citation: LIU Xiao-Yan, FENG Xiang-Chu, ZHAO Chen-Ping. Anisotropic Diffusion Model Based on Generalized Metric in Sobolev Space. ACTA AUTOMATICA SINICA, 2015, 41(2): 320-329. doi: 10.16383/j.aas.2015.c140564

Sobolev 广义度量下的各向异性扩散模型

doi: 10.16383/j.aas.2015.c140564
基金项目: 

国家自然科学基金(61271294,61362029,61379030,61472303)资助

详细信息
    作者简介:

    冯象初 西安电子科技大学数学与统计学院教授. 1999 年获西安电子科技大学理学博士学位. 主要研究方向为数值分析, 小波, 图像处理的偏微分方程方法.E-mail: xcfeng@mail.xidian.edu.cn

    通讯作者:

    刘孝艳 西安电子科技大学数学与统计学院博士研究生, 西安石油大学理学院讲师. 主要研究方向为变分及偏微分方程在图像处理中的应用. 本文通信作者.E-mail: feng2001410@163.com

Anisotropic Diffusion Model Based on Generalized Metric in Sobolev Space

Funds: 

Supported by National Natural Science Foundation of China (61271294, 61362029, 61379030, 61472303)

  • 摘要: 给出了Sobolev空间的一种广义度量, 在该度量下提出了一个新的各向异性增强、扩散方程. 广义度量中的变系数, 较好地控制了方程的扩散行为, 使得新模型不仅能有效增强图像的细节特征, 而且能在噪声去除和边缘保护之间取得较好的平衡, 同时给出了相应的隐式离散算法. 仿真实验结果表明, 新模型和算法是行之有效的.
  • [1] Witkin A P. Scale-space filtering. In: Proceedings of the 8th International Joint Conference on Artificial Intelligence. Karlsruhe, Germany: Morgan Kaufmann, 1983. 1019-1021
    [2] [2] Witkin A P. Scale-space filtering: a new approach to multi-scale description. In: Proceedings of the 1984 IEEE International Conference on Acoustics, Speech, and Signal Processing. San Diego, USA: IEEE, 1984. 150-153
    [3] [3] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639
    [4] [4] Calder J, Mansouri A, Yezzi A. Image sharpening via sobolev gradient flows. SIAM Journal on Imaging Sciences, 2010, 3(4): 981-1014
    [5] [5] Calder J, Mansouri A. Anisotropic image sharpening via well-posed Sobolev gradient flows. SIAM Journal on Mathematical Analysis, 2011, 43(4): 1536-1556
    [6] [6] Catt F, Lions P L, Morel J M, Coll T. Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal on Numerical Analysis, 1992, 29(1): 182-193
    [7] [7] You Y L, Kaveh M. Fourth-order partial differential equations for noise removal. IEEE Transactions on Image Processing, 2000, 9(10): 1723-1730
    [8] [8] Bai J, Feng X C. Fractional-order anisotropic diffusion for image denoising. IEEE Transactions on Image Processing, 2007, 16(10): 2492-2502
    [9] Zhai Dong-Hai, Yu Jiang, Duan Wei-Xia, Xiao Jie, Li Fan. Improved image denoising algorithm using UK-flag shaped anisotropic diffusion model. Journal of Computer Applications, 2014, 34(5): 1494-1498(翟东海, 鱼江, 段维夏, 肖杰, 李帆. 米字型各向异性扩散模型的图像去噪算法. 计算机应用, 2014, 34(5): 1494-1498)
    [10] Osher S, Rudin L I. Feature-oriented image enhancement using shock filters. SIAM Journal on Numerical Analysis, 1990, 27(4): 919-940
    [11] Alvarez L, Mazorra L. Signal and image restoration using shock filters and anisotropic diffusion. SIAM Journal on Numerical Analysis, 1994, 31(2): 590-605
    [12] Terebes R, Borda M, Germain C, Lavialle O. A novel shock filter for image restoration and enhancement. In: Proceedings of the 20th European Signal Processing Conference. Bucharest, Romania: IEEE, 2012. 255-259
    [13] Xu Huan-Yu, Sun Quan-Sen, Chen Qiang, Luo Nan, Xia De-Shen. Blind image restoration using half Gaussian kernel based shock-diffusion filter. Acta Automatica Sinica, 2014, 40(6): 1166-1175(徐焕宇, 孙权森, 陈强, 罗楠, 夏德深. 使用半高斯核的冲击扩散滤波图像盲复原方法. 自动化学报, 2014, 40(6):1166-1175)
    [14] Magnier B, Xu H, Montesinos P. Half Gaussian kernels based shock filter for image deblurring and regularization. In: Proceedings of the 8th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Barcelone, France: Springer, 2013.
    [15] Gilboa G, Sochen N, Zeevi Y Y. Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Transactions on Image Processing, 2002, 11(7): 689-703
    [16] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D, 1992, 60(1-4): 259-268
    [17] Ji X P, Zhang D Z, Guo Z C, Wu B Y. Image denoising via nonlinear hybrid diffusion. Mathematical Problems in Engineering, 2013, 2013, Article ID 890157
    [18] Zhang Shan-Qing, Zhang Kun-Long. Texture image segmentation model based on eigenvalues of structure tensor. Acta Electronica Sinica, 2013, 41(7): 1324-1328(张善卿, 张坤龙. 基于结构张量特征值的纹理图像分割模型. 电子学报, 2013, 41(7): 1324-1328)
    [19] Xiong Hui, Lai Jian-Huang. Equalized net diffusion model based on the anisotropy diffusion. Journal of Image and Graphics, 2013, 18(7): 731-737(熊辉, 赖剑煌. 各向异性的均衡化网状扩散模型. 中国图象图形学报, 2013, 18(7): 731-737)
    [20] Cai J F, Osher S, Shen Z W. Linearized Bregman iterations for compressed sensing. Mathematics of Computation, 2009, 78(267): 1515-1536
    [21] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600-612
    [22] Kazemi P, Danaila I. Sobolev gradients and image interpolation. SIAM Journal on Imaging Sciences, 2012, 5(2): 601- 624
    [23] Yang Xiu-Hong, Guo Bao-Long, Wu Xian-Xiang. Wavelet inpainting based on tensor diffusion. Acta Automatica Sinica, 2013, 39(7): 1071-1079(杨秀红, 郭宝龙, 吴宪祥. 基于张量扩散的小波域修复模型. 自动化学报, 2013, 39(7): 1071-1079)
    [24] Zhang Wen-Juan, Feng Xiang-Chu, Wang Xu-Dong. Mumford-Shah model based on weighted total generalized variation. Acta Automatica Sinica, 2012, 38(12): 1913- 1922(张文娟, 冯象初, 王旭东. 基于加权总广义变差的Mumford-Shah模型. 自动化学报, 2012, 38(12): 1913-1922)
    [25] Tang Li-Ming, Tian Xue-Quan, Huang Da-Rong, Wang Xiao-Feng. Image segmentation model combined with FCMS and variational level set. Acta Automatica Sinica, 2014, 40(6): 1233-1248(唐利明, 田学全, 黄大荣, 王晓峰. 结合FCMS与变分水平集的图像分割模型. 自动化学报, 2014, 40(6): 1233-1248)
  • 加载中
计量
  • 文章访问数:  1771
  • HTML全文浏览量:  87
  • PDF下载量:  1495
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-08-08
  • 修回日期:  2014-11-15
  • 刊出日期:  2015-02-20

目录

    /

    返回文章
    返回