基于原始-对偶算法的自适应加权广义全变差图像去模糊

杨爱萍; 张越; 王金斌; 何宇清

doi:doi:10.3788/LOP55.041003

激光与光电子学进展, 2018, 55 (4): 041003, 网络出版: 2018-09-11

基于原始-对偶算法的自适应加权广义全变差图像去模糊下载： 1159次

Adaptive Weighted Generalized Total Variation Image Deblurring Based on Primal-Dual algorithm

杨爱萍张越王金斌 ^*何宇清

作者单位

天津大学电气自动化与信息工程学院, 天津 300072

引用该论文

杨爱萍, 张越, 王金斌, 何宇清. 基于原始-对偶算法的自适应加权广义全变差图像去模糊[J]. 激光与光电子学进展, 2018, 55(4): 041003.

Aiping Yang, Yue Zhang, Jinbin Wang, Yuqing He. Adaptive Weighted Generalized Total Variation Image Deblurring Based on Primal-Dual algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041003.

参考文献

[1] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1/2/3/4): 259-268.

[2] 闫敬文, 谢婷婷, 彭鸿, 等. 基于L0范数正则项的运动图像去模糊[J]. 激光与光电子学进展, 2017, 54(2): 021005.

Yan J W, Xie T T, Peng H, et al. Motion image deblurring based on L0 norms regularization term[J]. Laser & Optoelectronics Progress, 2017, 54(2): 021005.

[3] 孙骁, 赵源萌, 邓朝, 等. 基于马尔可夫约束的被动式太赫兹图像复原[J]. 中国激光, 2014, 41(10): 1011001.

Sun X, Zhao Y M, Deng C, et al. Passive terahertz image restoration based on Markov constraint[J]. Chinese Journal of Lasers, 2014, 41(10): 1011001.

[4] Kim H, Chen J, Wang A, et al. Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction[J]. Physics in Medicine and Biology, 2016, 61(18): 6878.

[5] Chantas G, Galatsanos N P, Molina R, et al. Variational bayesian image restoration with a product of spatially weighted total variation image priors[J]. IEEE Transactions on Image Processing, 2010, 19(2): 351-362.

[6] Candès E J, Wakin M B, Boyd S P. Enhancing sparsity by reweighted minimization[J]. Journal of Fourier Analysis and Applications, 2008, 14(5/6): 877-905.

[7] 陈利霞, 宋国乡, 丁宣浩, 等. 改进的总变分去噪算法[J]. 光子学报, 2009, 38(4): 1001-1004.

Chen L X, Song G X, Ding X H, et al. Improved total variation algorithms to remove noise[J]. Acta Photonica Sinica, 2009, 38(4): 1001-1004.

[8] Min L, Feng C. Compressive sensing reconstruction based on weighted directional total variation[J]. Journal of Shanghai Jiaotong University (Science), 2017, 22(1): 114-120.

[9] Shama M G, Huang T Z, Liu J, et al. A convex total generalized variation regularized model for multiplicative noise and blur removal[J]. Applied Mathematics and Computation, 2016, 276: 109-121.

[10] 王志军, 于之靖, 马凯, 等. 一种自适应中值梯度倒数加权的图像滤波算法[J]. 激光与光电子学进展, 2017, 54(12): 121001.

Wang Z J, Yu Z Q, Ma K, et al. An image filtering algorithm based on adaptive median and gradient inverse weight[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121001.

[11] 曹硕, 黄利萍, 侯倍倍, 等. 基于模糊边缘补足的自适应非局部均值图像去噪算法[J]. 激光与光电子学进展, 2018, 55(1): 011003.

Cao S, Huang L P, Hou B B, et al. Self-adaptive non-local means image denoising algorithm based on fuzzy edge complement[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011003.

[12] 谭志国, 欧建平, 张军, 等. 一种层析深度图像去噪算法[J]. 光学学报, 2017, 37(5): 0510002.

Tan Z G, Ou J P, Zhang J, et al. A Laminar denoising algorithm for depth image[J]. Acta Optica Sinica, 2017, 37(5): 0510002.

[13] Bredies K, Kunisch K, Pock T. Total generalized variation[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 492-526.

[14] Knoll F, Bredies K, Pock T, et al. Second order total generalized variation(TGV) for MRI[J]. Magnetic Resonance in Medicine, 2011, 65(2): 480-491.

[15] Chambolle A. An algorithm for total variation minimization and applications[J]. Journal of Mathematical Imaging and Vision, 2004, 20(1): 89-97.

[16] Chambolle A, Pock T. A first-order primal-dual algorithm for convex problems with applications to imaging[J]. Journal of Mathematical Imaging and Vision, 2011, 40(1): 120-145.

[17] Tao M, Yang J. Alternating direction algorithms for total variation deconvolution in image reconstruction[J]. TR0918, Department of Mathematics, Nanjing University, 2009.

[18] Zhang X, 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.

[19] Zhang J, Zhao D, Xiong R, et al. Image restoration using joint statistical modeling in a space-transform domain[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(6): 915-928.

杨爱萍, 张越, 王金斌, 何宇清. 基于原始-对偶算法的自适应加权广义全变差图像去模糊[J]. 激光与光电子学进展, 2018, 55(4): 041003. Aiping Yang, Yue Zhang, Jinbin Wang, Yuqing He. Adaptive Weighted Generalized Total Variation Image Deblurring Based on Primal-Dual algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041003.

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