全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
电子学报  2014 

一种基于GNC和增广拉格朗日对偶的非凸非光滑图像恢复方法

, PP. 264-271

Keywords: 非凸非光滑,惩罚函数,增广拉格朗日对偶,逐步非凸方法,图像恢复

Full-Text   Cite this paper   Add to My Lib

Abstract:

逐步非凸方法(GNC)和增广拉格朗日对偶在非凸非光滑图像恢复中有较高的恢复性能.然而分别使用这两种方法时GNC不能够保证全局收敛,增广拉格朗日对偶不能获得有效的初始值.为克服上述缺陷,本文通过转换原始问题为等式约束优化问题推出了一种基于GNC和增广拉格朗日对偶的组合图像恢复方法,并对其收敛性严格证明.该方法不仅可以获得有效的初始值,同时不要求问题具有凸性和光滑性.更多地,一个自适应能量函数通过对偶迭代而得到.实验结果表明推出的方法可以有效地提高图像恢复质量和算法效率.

References

[1]  R C Gonzalez, R E Woods.Digital Image Processing[M].2th ed, London:Prentice Hall, 2002.
[2]  刘红毅, 韦志辉, 张峥嵘.自适应非局部patch 正则化图像恢复[J].电子学报, 2012, 40(3):513-517. Liu Hong-yi, Wei Zhi-hui, Zhang Zheng-rong.Adaptive nonlocal patch regularization for image restoration[J].Acta Electronica Sinica, 2012, 40(3):513-517.(in Chinese)
[3]  孙玉宝, 韦志辉, 吴敏, 肖亮, 费选.稀疏性正则化的图像泊松去噪算法[J].电子学报, 2011, 39(2):285-290. Sun Yu-bao, Wei Zhi-hui, Ww Min, Xiao Liang, Fei Xuan.Image poisson denoising using sparse representations[J].Acta Electronica Sinica, 2011, 39(2):285-290.(in Chinese)
[4]  A M Bruckstein, D L Donoho, M Elad.From sparse solutions of systems of equations to sparse modeling of signals and images[J].SIAM Review, 2009, 51(1):34-81.
[5]  F H Clarke.Optimization and Nonsmooth Analysis[M].New York:John Wiley and Sons, 1983.
[6]  J V Burke, D Henrion, A S Lewis, M L Overton.Stabilization via nonsmooth, nonconvex optimization[J].IEEE Transation on Automatic Control, 2006, 51(11):1760-1769.
[7]  L Bedini, I Gerace, A Tonazzini.A GNC algorithm forconstrained image reconstruction with continuous-value line process[J].Pattern Recognition Letters, 1994, 15(9):907-918.
[8]  M Nikolova.Markovian reconstruction using a GNC approach[J].IEEE Transation on Image Processing, 1999, 8(9):1204-1220.
[9]  M Nikolova, K N Michael, C P Tam.Fast nonconvex nosmooth minimization methods for image restoration and reconstruction[J].IEEE Transation on Image Processing, 2010, 19(12):3073-3088.
[10]  易翔, 王蔚然.一种概率自适应图像去噪模型[J].电子学报, 2005, 33(1):63-66. Yi Xiang, Wang Wei-ran, A probability model for adaptive image denoising[J].Acta Electronica Sinica, 2005, 33(1):63-66.(in Chinese)
[11]  R Buil, M A Piera, P B Luh.Improvement of Lagrangian relaxation convergence for production scheduling[J].IEEE Transation on Automatic Control, 2012, 9(1):137-147.
[12]  R N Gasimov.Augmented Lagrangian duality and nondifferentiable optimization methods in nonconvex programming[J].Journal of Global Optimization, 2002, 24(2):187-203.
[13]  R S Burachik, C Y Kaya.An update rule and a convergence result for a penalty function method[J].Journal of Industrial and Management Optimization, 2007, 3(2):381-398.
[14]  R T Rockafellar.Lagrange muitipliers and optimality[J].SIAM Review, 1993, 35(2):183-238.
[15]  Sun Wen-yu, Yuan Ya-xiang.Optimization Theory and Method:Nonlinear Programming[M].Springer, 2006.
[16]  P M Camerini, L Fratta, F Maffioli.On improving relaxation methods by modified gradient techniques[J].Mathematical Programming Study, 1975, 3(3):26-34.
[17]  M Held, R M Karp.The traveling salesman problem and minimum spanning trees:part Ⅱ[J].Mathematical Programming, 1971, 1(1):6-25.
[18]  A Chambolle.An algorithm for total variation minimization and Applications[J].Journal of Mathematical Imaging and Vision, 2004, 20(1-2):89-97.
[19]  K N Michael, Qi Li-qun, Yang Yu-fei, Huang Yu-mei.On semismooth Newton''s methods for totel variation minimization[J].Journal of Mathematical Imaging and Vision, 2007, 27(3):265-276.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133