期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于能量分布的自适应整体变分去噪方法

An adapted denoising model of total variation based on energy distribution
原文传递
导出
摘要 研究了整体变分去噪的机制,提出了一种基于能量分布的自适应整体变分去噪模型,该模型继承了传统整体变分去噪保边缘的优点,并能够根据图像区域能量分布的特征,在不同区域自适应地选择相应的规整化参数,进行不同强度的去噪,在去噪保边缘的同时,较好地保持了纹理细节,在一定程度上克服了传统整体变分方法的缺点. By studing denoising mechanism of ROF TV model,an adapted TV model based on the energy distribution is proposed. This model inherited the merit of ROF TV model in denoising and keeping edge. It can chooses the regulating parameter in the different region according to region energy distribution in the image, and denoise by this parameter. So it can hold the edge and texture while denoisng. This method overcomed the shortcoming of ROF model in some degree.
作者 庄红林 段鹏 何磊
机构地区 云南民族大学数学与计算机科学学院
出处 《云南大学学报（自然科学版）》 CAS CSCD 北大核心 2008年第4期350-354,共5页 Journal of Yunnan University(Natural Sciences Edition)
基金 云南省教育厅自然科学基金资助项目(5Y0590D)
关键词 整体变分 图像去噪 自适应 能量分布 total variation image denoise adaptive energy distribution
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献12

  • 1STRONG D, CHANT. Edge-preserving and scale-dependent properties of total variation regularization[C]. Inv Probl, 2003, 19: 165-187.
  • 2KRISHNAN D, PHAM Q V, ANDY M. Aprimal- dual active-set algorithm for bilaterally constrained total variation deblurring and piecewise constant mumford-shah segmentation problems[ EB/OL]. http://www. math. ucla. edu/applied/cam/index. shtml, 2007 - 12 - 24.
  • 3CHANT F, SHEN J, VESE L. Variational PDE models in image proeessing[J].Notices Amer Math Soc,2003,50: 14-26.
  • 4HU Jian-bo, WANG Hong-bao. Adaptive total variation based on feature scale[C]. Proceedings of World Academy of Science, Engineering and Technology, Volume 2,2005.
  • 5邵肖伟,刘政凯,宋璧.一种基于TV模型的自适应图像修复方法[J].电路与系统学报,2004,9(2):113-117. 被引量：53
  • 6CHEN Y, WUNDERLI T. Adaptive total variation for image restoration in BV space[J].Journal of Mathematical Analysis and Applications, 2002,272 ( 1 ) : 117-137.
  • 7RUDIN L, OSHER S,FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992,60: 259- 268.
  • 8CHANT F, SHEN J. Image processing and analysis: variational, PDE, wavelet and stochastic methods[ C]. SIAM, 2005.
  • 9张红英,彭启琮.变分图像复原中PDE的推导及其数值实现[J].计算机工程与科学,2006,28(6):44-46. 被引量：12
  • 10邹谋炎,刘小军.总变分图像复原方程的离散化方法[J].电子与信息学报,2001,23(9):861-867. 被引量：7

二级参考文献14

  • 1[1]Chan T, Shen F. Mathematical Models for Local Non-texture Inpaintings [J]. SIAM Journal on Applied Mathematics, 2002, 62: 1019-1043.
  • 2[2]Bertalmio M, et al. Image Inpainting [A]. SIGGRAPH [C]. 2000, 1: 417-424.
  • 3[3]Criminisi A, Perez P, Toyama K. Object Removal by Exemplar-Based Inpainting [J]. IEEE Proc. CVPR, 2003, 2: 721-728.
  • 4[4]Masnou S. Disocclusion: A Variational Approach Using Level Lines [J]. IEEE Trans. Image Processing, 2002, 11: 68-76.
  • 5[5]Masnou S, Morel J. Level Lines Based Disocclusion [A]. IEEE Proc. ICIP [C]. 1998, 3: 259-263.
  • 6[6]Levin A, Zomet A, Weiss Y. Learning How to Inpaint from Global Image Statistics [A]. International Conference on Computer Vision [C]. 2003, 1: 305-312.
  • 7T F Chan,J H Shen:A Good Image Model Eases Restoration[EB/OL].http://www.ima.umn.edu/preprints/feb02/1829.pdf,2004-02.
  • 8S Geman,D Geman.Stochastic Relaxation,Gibbs Distributions,and the Bayesian Restoration of Images[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1984,6(6):721-741.
  • 9L Rudin,S Osher,E Fatemi.Nonlinear Total Variation Based Noise Removal Algorithms[J].Physica D,1992,60:259-268.
  • 10L Rudin,S Osher.Total Variation Based Image Restoration with Free Local Constraints[A].Proc 1st IEEE Int'l Conf on Image Processing.Vol 1[C].1994.31-35.

共引文献69

云南大学学报（自然科学版）
2008年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部