Weisheng Dong (董伟生)
Weisheng Dong received the Bachelor degree in Communication Engineering from Huazhong University of Science and Technology, Wuhan, China in 2004, and received the Ph. D degree in Circuit and System from the Xidian University, Xi'an, China in 2011. In Sep. 2010 he joined the School of Electronic Engineering, Xidian University, as a Lecturer, and has been a professor since Aug. 2016.
His major research interests focus on the low-level vision, the inverse problems in image processing, and image compression. He has published over 40 papers in well-known conferences and journals. He received the Best Paper Award of VCIP 2010, and the Silver Medal in the iENA exhibition held at Nuremberg, Germany, Oct. 2010. I’ve an associate editor of IEEE Transactions On Image Processing since July 2015.
l Tao Huang, Weisheng Dong*, Xuemei Xie, Guangming Shi, and Xiang Bai, “Mixed noise removal via Laplacian scale mixture modeling and nonlocal low-rank approximation,” IEEE Trans. on Image Processing, in press, 2017. (Paper, Code) (State-of-the-art mixed noise removal algorithm!)
l Weisheng Dong, Fazuo Fu, Guangming Shi, and Xun Cao, Jinjian Wu, Guangyu Li, and Xin Li, “Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation”, IEEE Trans. On Image Processing, vol. 25, no. 5, pp. 2337-2352, May 2016. (Paper, Project, Code) (A very effective non-negative dictionary learning and sparse coding algorithm has been proposed!)
l W. Dong, G. Shi, Y. Ma, and X. Li, “Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture,” International Journal of Computer Vision (IJCV), vol. 114, no. 2, pp. 217-232, Sep. 2015. (Paper) (Denoising Code) (State-of-the-art Image Restoration performance!).
l Weisheng Dong, Guangyu Li, Guangming Shi, Xin Li, and Yi Ma, "Low-rank tensor approximation with Laplacian scale mixture modeling for multiframe image denoising", in Proc. IEEE Int. Conf. on Computer Vision (ICCV), 2015. (PDF)
l Yongbo Li, Weisheng Dong*, Guangming Shi, and Xuemei Xie, "Learning parametric distributions for image super-resolution: where patch matching meets sparse coding," in Proc. IEEE Int. Conf. on Computer Vision (ICCV), 2015. (PDF)
l W. Dong, G. Shi, X. Li, Y. Ma, and F. Huang, “Compressive sensing via nonlocal low-rank regularization”, IEEE Trans. on Image Processing, vol. 23, no. 8, pp. 3618-3632, 2014. (Paper) (Project&Code) (State-of-the-art CS reconstruction performance on both natural images and complex-valued MRI images!)
My Google Scholar Citation profile: http://scholar.google.com/citations?user=-g58LsoAAAAJ&hl=en
Last update: Oct. 24, 2015.