期刊文献+

图像关联度在全变分模型停止准则中的运用 被引量:1

OPERATION OF IMAGE RELATION IN STOP CRITERION OF TOTAL VARIATION MODEL
下载PDF
导出
摘要 以全变分去噪模型为基础,详细阐述全变分模型的构造,利用图像的最优关联度作为停止准则的参考,获得一种新型的全变分图像去噪模型。该模型不仅能够获得一种较好的迭代准则,同时,图像去噪后的效果接近最优值。实验结果表明,该算法能够获得较好PSNR值,而且算法的复杂度也降低。 Taking the total variation denoising model as the basis,in this paper we expatiate on the structure of total variation model.By using the optimal relation of image as the reference for stop criterion,we gain a novel denoising model of total variation image.The model can obtain a quite good iteration criterion,and meanwhile its effect also approaches the optimal value after the image is noise removed.Experimental results show that algorithm can get preferable PSNR (peak signal-to-noise ratio)value,the computation complexity is decreased as well.
出处 《计算机应用与软件》 CSCD 北大核心 2014年第2期195-197,213,共4页 Computer Applications and Software
基金 国家自然科学基金项目(61171077) 江苏省高校自然科学研究项目(12KJB510025 12KJB510026) 交通部应用基础研究项目(2011-319-813-510) 南通市引进人才项目(03080415 03080416) 南通大学创新人才基金项目(2009)
关键词 全变分 停止准则 最优关联度 Total variation Stop criterion Optimal relation
  • 引文网络
  • 相关文献

参考文献11

  • 1王大凯.图像处理的偏微分方程方法[M]{H}北京:科学出版社,2008.
  • 2Chan T F,Shen J. Image Processing and Analysis[A].2005.
  • 3Chen Y,Levine S,Rao M. Variable exponent,linear growth functionals in image processing[J].SIAMJ Appl Math,2006,(04):13831406.
  • 4Rudin L,Osher S,Fatemi E. Nonliner total variation based noise remov-al algorithm[J].Physica D,1992.259268.
  • 5李敏,冯象初,杨文杰.基于全变差和小波硬阈值的图像去噪[J].信号处理,2006,22(6):917-919. 被引量:5
  • 6熊保平,杜民.基于PDE图像去噪方法[J].计算机应用,2007,27(8):2025-2026. 被引量:11
  • 7Starck J L,Elad M,Donoho D L. ImageDecomposit ion viathe Combi-nation of Sparse Representations and a VariationalApproach[J].IEEE Trans Image Progressing,2005,(10):15701582.
  • 8Célia A Z Barcelos,MauríLio Boaventura,Evanivaldo C.SILVA JR. Edge detection and noise removal by use of a partial differential equation with automatic selection of parameters[J].Computational &Applied Mathe-matics,2005,(01):131150.
  • 9Bai J,Feng X C. Fractional-order anisotropic diffusion for image denois-ing[J].{H}IEEE Transactions on Image Processing,2007,(10):24922502.
  • 10艾海舟.数字图像处理.

二级参考文献18

  • 1鞠磊,郑德玲,翁贻方.基于细胞神经网的快速图像分割方法[J].北京工商大学学报(自然科学版),2005,23(5):32-34. 被引量:3
  • 2谢美华,王正明.基于边缘定向增强的各向异性扩散抑噪方法[J].电子学报,2006,34(1):59-64. 被引量:27
  • 3钱伟新,刘瑞根,王婉丽,祁双喜,王伟,程晋明.一种新的闪光照相CCD图像的扩散滤波方法[J].爆炸与冲击,2006,26(4):351-355. 被引量:2
  • 4P. Perona and J. Malik. Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:629 - 639,1990.
  • 5L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259 -268,1992.
  • 6J. Weickert. Anisotropic Diffusion in Image Processing.Teubner, Stuttgart, 1998.
  • 7A. Chambolle, R. A. Devore, N. Lee, and B. L. Lucier.Nonlinear wavelet image processing:variational problems,compression, and noise removal through wavelet shrinkage. IEEE Transactions on Image Processing,7(3) :319 -335 ,Mar. 1998.
  • 8D. Donoho. De-noising by soft thresholding. IEEE Transactions on Information Theory,41:613 - 627,1995.
  • 9S. MaUat. A Wavelet Tour of Signal Processing. Academic Press, San Diego, 1998.
  • 10T. F. Chan and H. M. Zhou. Total variation improved wavelet thresholding in inage compression. In Proc. Seventh International Conference on Image Processing, Vancouver, Canada,September 2000.

共引文献14

同被引文献5

引证文献1

;
使用帮助 返回顶部