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Fast dual minimization of the vectorial total variation norm and applications to color image processing
1.  Department of Mathematics, University of California, Los Angeles, CA 900951555, United States, United States 
[1] 
Feishe Chen, Lixin Shen, Yuesheng Xu, Xueying Zeng. The Moreau envelope approach for the L1/TV image denoising model. Inverse Problems & Imaging, 2014, 8 (1) : 5377. doi: 10.3934/ipi.2014.8.53 
[2] 
Weihong Guo, Jing Qin. A geometry guided image denoising scheme. Inverse Problems & Imaging, 2013, 7 (2) : 499521. doi: 10.3934/ipi.2013.7.499 
[3] 
Jingwei Liang, Jia Li, Zuowei Shen, Xiaoqun Zhang. Wavelet frame based color image demosaicing. Inverse Problems & Imaging, 2013, 7 (3) : 777794. doi: 10.3934/ipi.2013.7.777 
[4] 
Wei Zhu, XueCheng Tai, Tony Chan. Augmented Lagrangian method for a mean curvature based image denoising model. Inverse Problems & Imaging, 2013, 7 (4) : 14091432. doi: 10.3934/ipi.2013.7.1409 
[5] 
G. Mastroeni, L. Pellegrini. On the image space analysis for vector variational inequalities. Journal of Industrial & Management Optimization, 2005, 1 (1) : 123132. doi: 10.3934/jimo.2005.1.123 
[6] 
Michael Hintermüller, Monserrat RinconCamacho. An adaptive finite element method in $L^2$TVbased image denoising. Inverse Problems & Imaging, 2014, 8 (3) : 685711. doi: 10.3934/ipi.2014.8.685 
[7] 
Shi Yan, Jun Liu, Haiyang Huang, XueCheng Tai. A dual EM algorithm for TV regularized Gaussian mixture model in image segmentation. Inverse Problems & Imaging, 2019, 13 (3) : 653677. doi: 10.3934/ipi.2019030 
[8] 
Wei Wan, Haiyang Huang, Jun Liu. Local block operators and TV regularization based image inpainting. Inverse Problems & Imaging, 2018, 12 (6) : 13891410. doi: 10.3934/ipi.2018058 
[9] 
Fangfang Dong, Yunmei Chen. A fractionalorder derivative based variational framework for image denoising. Inverse Problems & Imaging, 2016, 10 (1) : 2750. doi: 10.3934/ipi.2016.10.27 
[10] 
Qiang Liu, Zhichang Guo, Chunpeng Wang. Renormalized solutions to a reactiondiffusion system applied to image denoising. Discrete & Continuous Dynamical Systems  B, 2016, 21 (6) : 18391858. doi: 10.3934/dcdsb.2016025 
[11] 
Juan Carlos De los Reyes, CarolaBibiane Schönlieb. Image denoising: Learning the noise model via nonsmooth PDEconstrained optimization. Inverse Problems & Imaging, 2013, 7 (4) : 11831214. doi: 10.3934/ipi.2013.7.1183 
[12] 
Jianhong (Jackie) Shen, Sung Ha Kang. Quantum TV and applications in image processing. Inverse Problems & Imaging, 2007, 1 (3) : 557575. doi: 10.3934/ipi.2007.1.557 
[13] 
Shouhong Yang. Semidefinite programming via image space analysis. Journal of Industrial & Management Optimization, 2016, 12 (4) : 11871197. doi: 10.3934/jimo.2016.12.1187 
[14] 
Jie Huang, Marco Donatelli, Raymond H. Chan. Nonstationary iterated thresholding algorithms for image deblurring. Inverse Problems & Imaging, 2013, 7 (3) : 717736. doi: 10.3934/ipi.2013.7.717 
[15] 
Kenji Nakanishi. Modified wave operators for the Hartree equation with data, image and convergence in the same space. Communications on Pure & Applied Analysis, 2002, 1 (2) : 237252. doi: 10.3934/cpaa.2002.1.237 
[16] 
Wenxiang Cong, Ge Wang, Qingsong Yang, Jia Li, Jiang Hsieh, Rongjie Lai. CT image reconstruction on a low dimensional manifold. Inverse Problems & Imaging, 2019, 13 (3) : 449460. doi: 10.3934/ipi.2019022 
[17] 
Zhao Yi, Justin W. L. Wan. An inviscid model for nonrigid image registration. Inverse Problems & Imaging, 2011, 5 (1) : 263284. doi: 10.3934/ipi.2011.5.263 
[18] 
NamYong Lee, Bradley J. Lucier. Preconditioned conjugate gradient method for boundary artifactfree image deblurring. Inverse Problems & Imaging, 2016, 10 (1) : 195225. doi: 10.3934/ipi.2016.10.195 
[19] 
Ruiliang Zhang, Xavier Bresson, Tony F. Chan, XueCheng Tai. Four color theorem and convex relaxation for image segmentation with any number of regions. Inverse Problems & Imaging, 2013, 7 (3) : 10991113. doi: 10.3934/ipi.2013.7.1099 
[20] 
Juan C. Moreno, V. B. Surya Prasath, João C. Neves. Color image processing by vectorial total variation with gradient channels coupling. Inverse Problems & Imaging, 2016, 10 (2) : 461497. doi: 10.3934/ipi.2016008 
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