Professor Raymond Hon-fu CHAN

	Address	(Office):	YEUNG-G5136, CityU
	Phone	(Office):	(852) 3442-2980
	Email		rchan.sci@cityu.edu.hk
	Web		https://scholars.cityu.edu.hk/en/persons/hon-fu-raymond-chan(98f1cf10-a01a-4642-8470-c48b0e977687).html

 

Education

	1985	Ph.D.	New York University, USA
	1984	M.Sc.	New York University, USA
	1980	B.Sc.(1st  Class Honour)	The Chinese University of Hong Kong (CUHK)

 

Position

• Chair Professor, Department of Mathematics
• Dean (CSCI), College of Science

 

Research Interests

• Deblurring and denoising of images for different noise models (Gaussian, impulse, Possion)
• High-resolution image reconstruction from still images and also from video sequences
• Image segmentation for various types of images and structures

 

Representative Publications

• Guo, X., Chan, R. H., Wong, W. & Zhu, L. (2019) Mean–variance, mean-VaR, and mean-CVaR models for portfolio selection with background risk. In : Risk Management. 21, 2, p. 73–98
• Cai, X., Chan, R., Nikolova, M. & Zeng, T. (2017) A Three-Stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT). In : Journal of Scientific Computing.72, 3, p. 1313-1332
• Yin, G., Chan, R. H. & Yeung, M. (2017) A FEAST algorithm with oblique projection for generalized eigenvalue problems. In : Numerical Linear Algebra with Applications. 24, 4, e2092.
• Bergmann, R., Chan, R. H., Hielscher, R., Persch, J. & Steidl, G., May (2016) Restoration of manifold-valued images by half-quadratic minimization. In : Inverse Problems and Imaging. 10, 2, p. 281-304
• Wen, Y., Chan, R. H. & Zeng, T. (2016) Primal-dual algorithms for total variation based image restoration under Poisson noise. In : Science China Mathematics. 59, 1, p. 141-160
• Chan, R. H., Liang, H., Wei, S., Nikolova, M. & Tai, X. (2015) High-order total variation regularization approach for axially symmetric object tomography from a single radiograph. In : Inverse Problems and Imaging. 9, 1, p. 55-77
• Chen, C., Chan, R. H., Ma, S. & Yang, J. (2015) Inertial Proximal ADMM for Linearly Constrained Separable Convex Optimization. In : SIAM Journal on Imaging Sciences. 8, 4, p. 2239-2267
• Wen, J., Chan, R. H. F., Yau, S., He, R. L. & Yau, S. S. (2014) K-mer natural vector and its application to the phylogenetic analysis of genetic sequences. In : Gene. 546, 1, p. 25-34
• Xi, Y., Xia, J. & Chan, R. (2014) A Fast Randomized Eigensolver with Structured LDL Factorization Update. In : SIAM Journal on Matrix Analysis and Applications.35, 3, p. 974-996
• Chan, R., Yang, H. & Zeng, T. (2014) A Two-Stage Image Segmentation Method for Blurry Images with Poisson or Multiplicative Gamma Noise. In : SIAM Journal on Imaging Sciences. 7, 1, p. 98-127