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一种基于
L 1范数的非局部变分图像复原模型A non local total variation based on
L 1 norm for image recovery- 液晶与显示 2017年32卷第8期 页码：635-641
- 作者机构：
四川理工学院 自动化与电子信息学院, 四川 自贡 643000
- 作者简介：
[ "杨平先(1961-), 男, 四川自贡人, 硕士研究生, 教授, 1990年毕业于重庆大学获硕士学位, 现为四川理工学院研究生导师, 主要研究工作是信息处理。联系方式:E-mail:ypingx@163.com" ]
陈明举, E-mail:mailcmj@163.com
- 基金信息：四川省教育厅项目(17ZB0302);人工智能四川省重点实验室开放基金项目(2015RZY01);人工智能四川省重点实验室开放基金项目(2016RYY02);四川理工学院科研项目(2015RC16);the Scientific Research Project of Sichuan University of Science and Engineering(2015RC16);Open Fund Project of Artificial Intelligence Key Laboratory of Sichuan Province(2015RZY01);Open Fund Project of Artificial Intelligence Key Laboratory of Sichuan Province(2016RYY02);Project of Sichuan Provincial Department of Education(17ZB0302)
DOI：10.3788/YJYXS20173208.0635
中图分类号： TN911.73收稿日期：2017-02-23，
录用日期：2017-5-15，
纸质出版日期：2017-08-05
- 稿件说明：
移动端阅览
杨平先, 陈明举. 一种基于
L 1范数的非局部变分图像复原模型[J]. 液晶与显示, 2017,32(8):635-641.Ping-xian YANG, Ming-ju CHEN. A non local total variation based on
L 1 norm for image recovery[J]. Chinese journal of liquid crystals and displays, 2017, 32(8): 635-641.杨平先, 陈明举. 一种基于
L 1范数的非局部变分图像复原模型[J]. 液晶与显示, 2017,32(8):635-641. DOI： 10.3788/YJYXS20173208.0635.Ping-xian YANG, Ming-ju CHEN. A non local total variation based on
L 1 norm for image recovery[J]. Chinese journal of liquid crystals and displays, 2017, 32(8): 635-641. DOI： 10.3788/YJYXS20173208.0635.
摘要
针对
L
2
范数的非局部变分模型在迭代过程中未考虑图像局部梯度信息，模糊图像细节信息的缺点，提出了一种基于
L
1
范数的非局部变分模型。首先，对基于
L
1
范数的非局部变分模型的扩散性能进行了详细的分析。接着，将该模型应用于退化图像的复原中，并推导出该模型的Bregman交替迭代求解过程。最后，通过对比实验，证明本文提出的
L
1
范数的非局部变分复原模型能更好地重构图像的细节信息，相对于
L
2
范数的非局部变分模型峰值信噪比提高大于1 dB，图像复原性能更优。
Abstract
To overcome the disadvantages that non local total variation based on
L
2
norm doesn't consider the nonlocal gradient and can't effectively recovery image's texture
a new non local total variation based on
L
1
norm is proposed. The paper first analyzes the characteristics of the new algorithm
and then employs the new algorithm for image recovery. Bregman iteration scheme is used to optimize the image recovery problems. Finally
the comparing experimental results show that the proposed non local total variation model based on
L
1
norm reconstructs more image details and gains more 1 dB higher peak signal to noise ratio than non local total variation model based on
L
2
norm.
关键词
Keywords
references
RUDIN L I, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D : Nonlinear Phenomena , 1992, 60(1/4):259-268.
LI B, LIU X P, GONG D X, et al . Image Inpainting Under Local Coordinate System [M]//YANG Y, MA M. Green Communications and Networks . Dordrecht:Springer, 2012:269-275.
WEN Y W, CHAN R H, YIPA M. A primal-dual method for total-variation-based wavelet domain inpainting[J]. IEEE Transactions on Image Processing , 2012, 21(1):106-114.
FU S J, RUAN Q Q, WANG W Q, et al . A feature-dependent fuzzy bidirectional flow for adaptive image sharpening[J]. Neurocomputing , 2007, 70(4/6):883-895.
唐玲, 陈明举.基于图像局部方向特性的自适应全变分去噪模型[J].液晶与显示, 2016, 31(5):477-483.
TANG L, CHEN M J. An adaptive directional total variationdenoising model based on image local direction[J]. Chinese Journal of Liquid Crystals and Displays , 2016, 31(5):477-483. (in Chinese)
GILBOA G, ZEEVI YY, SOCHEN N. Texture preserving variational denoising using an adaptive fidelity term[C]// Proceedings of VLSM . Nice, France:VLSM, 2003.
陈明举.基于统计特性的非局部均值去噪算法[J].液晶与显示, 2014, 29(3):450-454.
CHEN M J. Non-local means imagedenoising algorithm based on statistical property[J]. Chinese Journal of Liquid Crystals and Displays , 2014, 29(3):450-454. (in Chinese)
蔡强, 郝佳云, 曹健, 等.结合局部特征及全局特征的显著性检测[J].光学精密工程, 2017, 25(3):772-778.
CAI Q, HAO J Y, CAO J, et al . Salient detection via local and global feature[J]. Optics and Precision Engineering , 2017, 25(3):772-778. (in Chinese)
BUADES A, COLL B, MOREL J M. Image enhancement by non-local reverse heat equation[J]. Preprint Centre De Mathé matiques et Leurs Applications , 2006.
GILBOA G, OSHER S. Nonlocal operators with applications to image processing[J]. Multiscale Modeling & Simulation , 2008, 7(3):1005-1028.
LOU Y F, ZHANG X Q, OSHER S, et al . Image recovery via nonlocal operators[J]. Journal of Scientific Computing , 2010, 42(2):185-197.
PEYRÉ G, BOUGLEUX S, COHEN L. Non-local regularization of inverse problems[M]// European Conference on Computer Vision . Berlin Heidelberg:Springer, 2008, 5304:57-68.
ZHANG X Q, BURGER M, BRESSON X, et al . Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J]. SIAM Journal on Imaging Sciences , 2010, 3(3):253-276.
GILBOA G, SOCHEN N, ZEEVI YY. Estimation of optimal PDE-based denoising in the SNR sense[J]. IEEE Transactions on Image Processing , 2006, 15(8):2269-2280.
CAI J F, OSHER S, SHEN Z W. Convergence of the linearized bregman iteration for l 1 -norm minimization[J]. Mathematics of Computation , 2009, 78(268):2127-2136.
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