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全变分去噪模型参数的选择研究

Research of TV Denoising Model Parameter Selection
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摘要 针对全变分模型对参数选取十分敏感的问题,根据模型推导出计算全变分模型最优参数的公式。在传统迭代公式中,添加该公式,利用迭代的方法不断逼近参数最优解,使图像去噪结果达到最优。实验结果表明,当参数初值在大范围内变化时该方法仍能保持全变分模型解的稳定性。 For the problem that total variation model is very sensitive to parameter selection, the formulaused to get optimal parameter of TV model is proposed according to total variation model. The methodadds this formula to traditional iterative formulas, and then approaches to optimal parameter consistentlyby iteration to get best denoised image. The experiments show that the method can make solution of TVmodel steady when the parameters are changed in a large range.
出处 《青岛大学学报(自然科学版)》 CAS 2016年第3期41-46,共6页 Journal of Qingdao University(Natural Science Edition)
基金 国家自然科学基金(批准号:61170106)资助 山东省高等学校科技计划项目(批准号:J14LN39)资助 青岛市科技计划项目(批准号:13-1-4-156-jch)资助
关键词 图像去噪 自适应参数 全变分模型 变分法 image denoising self-adaptive parameter TV model variation method
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  • 1Leonid I.RUDIN,Stanley OSHER,Emad FATEMI.Nonlinear total variation based noise removal algorithms[J].Phys.D,1992,60(1-4):259-268.
  • 2Bing SONG.Topics in Variational PDE Image Segmentation,Inpainting and Denoising[D].USA:University of California Los Angeles,2003.
  • 3Marino BELLONI,Bernd KAWOHL.A direct uniqueness proof for equations involving the p-Laplace operator[J].Manuscripta math,2002,109(2):229-231.
  • 4Bernd KAWOHL.From Mumford-Shah to Perona-Malik in image processing[J].Math.Methods Appl.Sci,2004,27(15):1803-1814.
  • 5Bernd KAWOHL,Jana STARA.Observations on a nonlinear evolution equation[EB/OL].http://www.karlin.mff.cuni.cz/-rokyta/ preprint/2004-pap/2004-139.ps,2004-12-20.
  • 6Chan T F, Shen J. hnage P:veessing and Analysis, Variational, PDE, Wavelet and Slochastic Methods [ M ]. Philadelphia, USA: SIAM, 2005.
  • 7Aubert G, Kornprol:sl P. Mathematical Problems in hnage Pro- eessing: Pa:lial Differential Equations and the Calculus of Varia- tions[M]. 2nd ed. New York: Springer, 2006.
  • 8Tikhonov A N. Regularization of incorrectly posed problems[J]. Soviet Matbematies Doklady, 1963, 4 (6) : 1624-1627.
  • 9Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Pbysica D, 1992, 60(1-4) :259-268.
  • 10Chambolle A, l,ions P L. Image reeoveL'y via total variation mini- mization and related p:x:blems [J]. Nmnerisehe Mathematik, 1997, 76(2) :167-188.

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