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Variational denoising of diffusion weighted MRI
1. | West Virginia University, Morgantown, WV 26506, United States |
2. | University of Florida, Gainesville, FL 32601, United States, United States, United States |
3. | National Institutes of Health, Bethesda, MD 20892, United States |
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Yuyuan Ouyang, Yunmei Chen, Ying Wu. Total variation and wavelet regularization of orientation distribution functions in diffusion MRI. Inverse Problems and Imaging, 2013, 7 (2) : 565-583. doi: 10.3934/ipi.2013.7.565 |
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Elie Bretin, Imen Mekkaoui, Jérôme Pousin. Assessment of the effect of tissue motion in diffusion MRI: Derivation of new apparent diffusion coefficient formula. Inverse Problems and Imaging, 2018, 12 (1) : 125-152. doi: 10.3934/ipi.2018005 |
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Zhiguang Zhang, Qiang Liu, Tianling Gao. A fast explicit diffusion algorithm of fractional order anisotropic diffusion for image denoising. Inverse Problems and Imaging, 2021, 15 (6) : 1451-1469. doi: 10.3934/ipi.2021018 |
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Braxton Osting, Dong Wang. Diffusion generated methods for denoising target-valued images. Inverse Problems and Imaging, 2020, 14 (2) : 205-232. doi: 10.3934/ipi.2020010 |
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Qiang Liu, Zhichang Guo, Chunpeng Wang. Renormalized solutions to a reaction-diffusion system applied to image denoising. Discrete and Continuous Dynamical Systems - B, 2016, 21 (6) : 1839-1858. doi: 10.3934/dcdsb.2016025 |
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Abdelghafour Atlas, Mostafa Bendahmane, Fahd Karami, Driss Meskine, Omar Oubbih. A nonlinear fractional reaction-diffusion system applied to image denoising and decomposition. Discrete and Continuous Dynamical Systems - B, 2021, 26 (9) : 4963-4998. doi: 10.3934/dcdsb.2020321 |
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Ying Wen, Jiebao Sun, Zhichang Guo. A new anisotropic fourth-order diffusion equation model based on image features for image denoising. Inverse Problems and Imaging, 2022, 16 (4) : 895-924. doi: 10.3934/ipi.2022004 |
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Robert D. Sidman, Marie Erie, Henry Chu. A method, with applications, for analyzing co-registered EEG and MRI data. Conference Publications, 2001, 2001 (Special) : 349-356. doi: 10.3934/proc.2001.2001.349 |
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Xianchao Xiu, Lingchen Kong. Rank-one and sparse matrix decomposition for dynamic MRI. Numerical Algebra, Control and Optimization, 2015, 5 (2) : 127-134. doi: 10.3934/naco.2015.5.127 |
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Ryan Compton, Stanley Osher, Louis-S. Bouchard. Hybrid regularization for MRI reconstruction with static field inhomogeneity correction. Inverse Problems and Imaging, 2013, 7 (4) : 1215-1233. doi: 10.3934/ipi.2013.7.1215 |
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Weihong Guo, Jing Qin. A geometry guided image denoising scheme. Inverse Problems and Imaging, 2013, 7 (2) : 499-521. doi: 10.3934/ipi.2013.7.499 |
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Yunho Kim, Paul M. Thompson, Luminita A. Vese. HARDI data denoising using vectorial total variation and logarithmic barrier. Inverse Problems and Imaging, 2010, 4 (2) : 273-310. doi: 10.3934/ipi.2010.4.273 |
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Mujibur Rahman Chowdhury, Jun Zhang, Jing Qin, Yifei Lou. Poisson image denoising based on fractional-order total variation. Inverse Problems and Imaging, 2020, 14 (1) : 77-96. doi: 10.3934/ipi.2019064 |
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Haijuan Hu, Jacques Froment, Baoyan Wang, Xiequan Fan. Spatial-Frequency domain nonlocal total variation for image denoising. Inverse Problems and Imaging, 2020, 14 (6) : 1157-1184. doi: 10.3934/ipi.2020059 |
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Fangfang Dong, Yunmei Chen. A fractional-order derivative based variational framework for image denoising. Inverse Problems and Imaging, 2016, 10 (1) : 27-50. doi: 10.3934/ipi.2016.10.27 |
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Rongliang Chen, Jizu Huang, Xiao-Chuan Cai. A parallel domain decomposition algorithm for large scale image denoising. Inverse Problems and Imaging, 2019, 13 (6) : 1259-1282. doi: 10.3934/ipi.2019055 |
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Wei Zhu, Xue-Cheng Tai, Tony Chan. Augmented Lagrangian method for a mean curvature based image denoising model. Inverse Problems and Imaging, 2013, 7 (4) : 1409-1432. doi: 10.3934/ipi.2013.7.1409 |
[18] |
Yoon Mo Jung, Taeuk Jeong, Sangwoon Yun. Non-convex TV denoising corrupted by impulse noise. Inverse Problems and Imaging, 2017, 11 (4) : 689-702. doi: 10.3934/ipi.2017032 |
[19] |
Feishe Chen, Lixin Shen, Yuesheng Xu, Xueying Zeng. The Moreau envelope approach for the L1/TV image denoising model. Inverse Problems and Imaging, 2014, 8 (1) : 53-77. doi: 10.3934/ipi.2014.8.53 |
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Nils Dabrock, Yves van Gennip. A note on "Anisotropic total variation regularized $L^1$-approximation and denoising/deblurring of 2D bar codes". Inverse Problems and Imaging, 2018, 12 (2) : 525-526. doi: 10.3934/ipi.2018022 |
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