Wuchunlin

University of Science and Technology of China   Ph.D.   2001/09-2006/07;

University of Science and Technology of China   Bachlor  1997/09-2001/07;

Nankai University                                                  Associate Professor   2012/12 - ；

National University of Singapore                           Research Fellow       2010/07 - 2012/07；

Nanyang Technological University                                                      Research Fellow                   2008/03 - 2010/06；

Dr. Chunlin Wu is currently an associate professor of Nankai University. He obtained his bachlor and Ph.D.degrees from University of Science and Technology of China in 2001 and 2006, respectively. After several postdoc positions in University of Science and Technology of China, Nanyang Technological Universtiy, National University of Singapore, he joined Nankai University in 2012. His research interests include image and geometry computation, numerical approximation and optimization. He has published over 20 papers, including several in important area-related journals like SIAM J. Imaging Sciences, SIAM J. Numerical Analysis, Inverse Problems, Adv. Comput. Math., J. Sci. Comput., Inverse Prob. Imaging, ACM TOG, IEEE TVCG,IEEE TIP, IEEE TMI. 

1. National Natural Science Foundation of China Youth Program, 2014/01-2016/12, Principle Investigator;

2. National Natural Science Foundation of China General Program,  2019/01-2022/12,    Principle Investigator;

3. National Natural Science Foundation of China Key Program,          2016/01-2020/12, Co-Principle Investigator;

Publications:

  

1. C. Zeng, R. Jia, C. Wu. An Iterative Support Shrinking Algorithm for Non-Lipschitz Optimization in Image Restoration. J. Mathematical Imaging and Vision, available online 2018.

  

2. W. Wang, S. Wen, C. Wu, J. Deng. Denoising Piecewise Constant Images with Selective Averaging and Outlier Removal. Numerical Mathematics: Theory, Methods and Applications, available online 2018. 

  

3. C. Zeng, C. Wu, On the discontinuity of images recovered by noncovex nonsmooth regularized isotropic models with box constraints. Adv. Comput. Math., available online 2018.

  

4. C. Zeng, C. Wu, On the edge recovery property of noncovex nonsmooth regularization in image restoration. SIAM J. Numer. Anal., 56:1168-1182, 2018.  

  

5. H. Zhang, C. Wu, J. Deng, Z. Liu, Y. Yang. A new two-stage mesh surface segmentation method. The Visual Computer,34: 1597-1615, 2018.

  

6. C. Wu, Z. Liu, S. Wen, A general truncated regularization framework for contrast-preserving variational signal and image restoration: motivation and implementation. SCI. CHINA Mathematics., 61:1711-1732, 2018.

  

7. X. Feng, C. Wu, C. Zeng, On the local and global minimizers of L0 gradient regularized model with box constraints for image restoration. Inverse Problems, 34, 095007, 2018.

  

8. W. Wang, C. Wu, J. Deng, A general selective averaging method for piecewise constant signal and image processing. J. Scientific Computing., 76:1078-1104, 2018.

  

9. S. Wali, Z. Liu, C. Wu, H. Chang. A boosting procedure for variational-based image restoration. Numerical Mathematics: Theory, Methods and Applications,11:49-73, 2018.

  

10. Z. Liu, H. Zhang, C. Wu, On geodesic curvature flow with level set formulation over triangulated surfaces, J. Scientific Computing., 70:631-661, 2017.

  

11. H. Chang,W. Huang, S.Huang, C. Guan, C. Wu, S. Sekar, K. K. Bhakoo, Y. Duan. A new variational method for bias correction and its applications to rodent brain extraction. IEEE Trans. Medical Imaging., 36: 721-733, 2017.

  

12. H. Zhang, C. Wu, J. Zhang, J. Deng. Variational mesh denoising using total variation and piecewise constant function space. IEEE Trans. Visualization and Computer Graphics.,21: 873-886, 2015.

  

13. Y. Duan, H. Chang, W. Huang, J. Zhou, Z. Lu, C. Wu. The L0 regularized Mumford–Shah model for bias correction and segmentation of medical images. IEEE Trans. Image Processing, 24: 3927-3938,2015.

  

14. C. Wu, On the continuity of images by transmission imaging. Communications in Mathematical Sciences., 11: 573-595, 2013.

  

15. J. Zhang, J. Zheng, C. Wu, J. Cai. Variational mesh decomposition. ACM Trans. Graphics, 31(3): article no. 21, 2012.

  

16. C. Wu, J. Zhang, Y. Duan, X.C. Tai. Augmented Lagrangian method for total variation based image restoration and segmentation over triangulated surfaces. J. Scientific Computing, 50: 145-166, 2012.

  

17. J. Hahn, C. Wu, X.C. Tai. Augmented Lagrangian method for generalized TV-Stokes model. J.Scientific Computing, 50: 235-264, 2012.

  

18. C. Wu, J. Zhang, X.C. Tai. Augmented Lagrangian method for total variation restoration with non-quadratic fidelity. Inverse Problems and Imaging., 5:237-261, 2011.

  

19. C. Wu,X.C. Tai. Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models. SIAM J. Imaging Sciences., 3: 300-339, 2010.

  

20. C. Wu, X.C. Tai. A level set formulation of geodesic curvature flow on simplicial surfaces. IEEE Trans. Visualization and Computer Graphics, 16: 647–662, 2010.

  

21. C. Wu, J.Deng, F.Chen,X.C. Tai. Scale-space analysis of discrete filtering over arbitrary triangulated surfaces. SIAM J. Imaging Sciences., 2: 670-709, 2009.

  

22. H. Han, M. Yan, C. Wu. An energy regularization method for backward diffusion problem and its applications to image deblurring. Commun. Comput. Physics, 4: 177-194, 2008.

  

23. C. Wu, J. Deng, F. Chen. Fast data extrapolating. J. Comput. Appl. Math., 206: 146-157, 2007.