Total curvature (TC) model and its alternating direction method of multipliers algorithm for noise removal
This paper develops a variational model for image noise removal using total curvature (TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the characteristics of nonlinear, non-convex and non-smooth. Consequently, the numerical com...
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| Published in | Optoelectronics letters Vol. 15; no. 3; pp. 217 - 223 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Tianjin
Tianjin University of Technology
01.05.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1673-1905 1993-5013 |
| DOI | 10.1007/s11801-019-8145-y |
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| Abstract | This paper develops a variational model for image noise removal using total curvature (TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the characteristics of nonlinear, non-convex and non-smooth. Consequently, the numerical computation with the curvature regularization is difficult. In order to conquer the computation problem, the proposed model is transformed into an alternating optimization problem by importing auxiliary variables. Furthermore, based on alternating direction method of multipliers, we design a fast numerical approximation iterative scheme for proposed model. Finally, numerous experiments are implemented to indicate the advantages of the proposed model in image edge preserving, image contrast and corners preserving. Meanwhile, the high computational efficiency of the designed model is verified by comparing with traditional models, including the total variation (TV) and total Laplace (TL) model. |
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| AbstractList | This paper develops a variational model for image noise removal using total curvature (TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the characteristics of nonlinear, non-convex and non-smooth. Consequently, the numerical computation with the curvature regularization is difficult. In order to conquer the computation problem, the proposed model is transformed into an alternating optimization problem by importing auxiliary variables. Furthermore, based on alternating direction method of multipliers, we design a fast numerical approximation iterative scheme for proposed model. Finally, numerous experiments are implemented to indicate the advantages of the proposed model in image edge preserving, image contrast and corners preserving. Meanwhile, the high computational efficiency of the designed model is verified by comparing with traditional models, including the total variation (TV) and total Laplace (TL) model. |
| Author | Huang, Bao-xiang Xue, Chao Wang, Ming-lei Wang, Yu-xi Mu, Yun-ping |
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BSKalavathiPMixed Noise Removal Using Hybrid Fourth Order Mean Curvature Motion, Springer International Publishing2016 – reference: PardoEKapolkaMJournal of Computational Physics20173443392017JCoPh.344..339P365674410.1016/j.jcp.2017.05.001 – reference: HinterbergerWScherzerOComputing200676109217467410.1007/s00607-005-0119-1 – reference: XiangliNHongQBoZXiayuanHIEEE Transactions on Image Processing2016252620349365310.1109/TIP.2016.2552402 – reference: MarkNDavidMThe 2.1-D Sketch, International Conference on Computer Vision1990 – volume: 14 start-page: 226 year: 2018 ident: 8145_CR1 publication-title: Optoelectronics Letters doi: 10.1007/s11801-018-8033-x – volume-title: Image Denoising via group Sparsity Residual Constraint year: 2017 ident: 8145_CR2 – volume: 37 start-page: 183 year: 1999 ident: 8145_CR7 publication-title: Machine Learning doi: 10.1023/A:1007665907178 – volume: 9 start-page: 1723 year: 2000 ident: 8145_CR18 publication-title: IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society doi: 10.1109/83.869184 – volume: 60 start-page: 259 year: 1992 ident: 8145_CR13 publication-title: Nonlinear Phenomena doi: 10.1016/0167-2789(92)90242-F – volume: 39 start-page: 4208 year: 2016 ident: 8145_CR25 publication-title: Mathematical Methods in the Applied Sciences doi: 10.1002/mma.3858 – volume: 69 start-page: 323 year: 2017 ident: 8145_CR17 publication-title: Digital Signal Processing doi: 10.1016/j.dsp.2017.07.001 – volume: 40 start-page: 291 year: 2006 ident: 8145_CR10 publication-title: Applied Intelligence – volume: 57 start-page: 1 year: 2017 ident: 8145_CR19 publication-title: Journal of Mathematical Imaging & Vision doi: 10.1007/s10851-016-0662-8 – volume-title: The 2.1-D Sketch, International Conference on Computer Vision year: 1990 ident: 8145_CR24 – volume: 78 start-page: 42 year: 2018 ident: 8145_CR12 publication-title: Digital Signal Processing doi: 10.1016/j.dsp.2018.03.019 – volume: 344 start-page: 339 year: 2017 ident: 8145_CR8 publication-title: Journal of Computational Physics doi: 10.1016/j.jcp.2017.05.001 – volume-title: Mixed Noise Removal Using Hybrid Fourth Order Mean Curvature Motion, Springer International Publishing year: 2016 ident: 8145_CR22 – volume: 4 start-page: 490 year: 2006 ident: 8145_CR4 publication-title: Siam Journal on Multiscale Modeling & Simulation – volume: 45 start-page: 516 year: 2017 ident: 8145_CR16 publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2017.01.009 – volume: 7 start-page: 1409 year: 2017 ident: 8145_CR23 publication-title: Inverse Problems & Imaging – volume: 25 start-page: 2620 year: 2016 ident: 8145_CR9 publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2016.2552402 – ident: 8145_CR3 – volume: 9 start-page: 55 year: 2015 ident: 8145_CR15 publication-title: Inverse Problems & Imaging doi: 10.3934/ipi.2015.9.55 – volume-title: Image Quality Metrics: PSNR vs. SSIM, International Conference on Pattern Recognition year: 2010 ident: 8145_CR26 – volume: 49 start-page: 162 year: 2016 ident: 8145_CR21 publication-title: Digital Signal Processing doi: 10.1016/j.sigpro.2015.09.026 – volume: 9 start-page: 19 year: 2016 ident: 8145_CR6 publication-title: Statistical Methodology doi: 10.1016/j.stamet.2011.04.003 – volume: 44 start-page: 1001 year: 2014 ident: 8145_CR5 publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2013.2278548 – volume: 159 start-page: 253 year: 2016 ident: 8145_CR14 publication-title: Mathematical Programming doi: 10.1007/s10107-015-0957-3 – volume: 76 start-page: 109 year: 2006 ident: 8145_CR20 publication-title: Computing doi: 10.1007/s00607-005-0119-1 – volume: 114 start-page: 188 year: 2017 ident: 8145_CR11 publication-title: Applied Numerical Mathematics doi: 10.1016/j.apnum.2016.04.011 |
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| Snippet | This paper develops a variational model for image noise removal using total curvature (TC), which is a high-order regularizer. The TC has the advantage of... |
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| SubjectTerms | Algorithms Computing time Curvature Image contrast Iterative methods Lasers Mathematical models Multipliers Numerical analysis Optical Devices Optics Optimization Photonics Physics Physics and Astronomy Regularization |
| Title | Total curvature (TC) model and its alternating direction method of multipliers algorithm for noise removal |
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