Weisheng Dong (董伟生)
Shot bio: I’m a professor of School of artificial intelligence, Xidian University. I received the Bachlor degree from Huazhong University of Science and Technology (HUST), Wuhan, China, and Ph. D. degree from Xidian University, Xi’an, China. My research interests include low-level vision, inverse problems, computer vision and deep learning. I’m an associate editor of IEEE T-IP and SIAM J. on Imaging Sciences. I’m currently a Chang Jiang Scholar Professor (youth program) of China Ministry of Education, a Top-notch Talent Young Scholar of National Ten Thousand Talents Program, and also supported by the Excellent Young Scientist Foundation of NSFC of 2016.
Opening positions: 1) I’m looking for self-motivated Ph. D./M. S. students with solid background in programming and mathematics. 2) I’m also looking for several PostDocs / fresh faculty members with strong research background in computer vision, image processing, and deep learning, to join my group. Please send me email if you have interests.
l T. Huang, W. Dong, X. Yuan, J. Wu, and G. Shi, “Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging,” IEEE CVPR 2021. (Paper, Project & Code) (Deep Gaussian Scale Mixture network was proposed to learn the parametric image distributions, leading to state-of-the-art Spectral image reconstruction performance!)
l Q. Ning, W. Dong, G. Shi, L. Li and X. Li, “Accurate and lightweight image super-resolution with model-guided deep unfolding network,” IEEE Journal of Selected Topics on Signal Processing, vol. 15, no. 2, pp. 240-252, Feb. 2021. (Paper, Code, Github) (A deep nonlocal auto-regressive model is imbedded into the network obtaining state-of-the-art image SR performance!)
l T. Huang, W. Dong, J. Liu, F. Wu, G. Shi, and X. Li, “Accelerating convolutional neural network via structured Gaussian scale mixture models: a joint grouping and pruning approach,” IEEE Journal of Selected Topics on Signal Processing, vol. 14, no. 4, pp. 817-827, May, 2020. (Paper, Code)
l Q. Ning, W. Dong, F. Wu, J. Wu, J. Lin, and G. Shi, “Spatial-temporal Gaussian scale mixture modeling for foreground estimation,” AAAI 2020. (Paper, code coming soon)
l W. Dong, H. Wang, F. Wu, G. Shi, and X. Li, “Deep spatial-spectral representation learning for hyperspectral image denoising”, IEEE Trans. on Computational Imaging, vol. 5, no. 4, pp. 635-648, 2019. (Paper, code)
l Weisheng Dong, P. Wang, W. Yin, G. Shi, F. Wu, and X. Lu, “Denoising Prior Driven Deep Neural Network for Image Restoration” IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 41, no. 10, pp. 2308-2318, Oct., 2019. (Paper) (Code)
l Y. Li, Weisheng Dong*, X. Xie, G. Shi, J. Wu, and X. Li, “Image Super-resolution with Parametric Sparse Model Learning”, IEEE Trans. on Image Processing, vol. 27, no. 9, pp. 4638-4650, Sep., 2018. (Paper)
l G. Shi, T. Huang, Weisheng Dong*, J. Wu, and X. Xie, “Robust Foreground Estimation via Structured Gaussian Scale Mixture Modeling”, IEEE Trans. on Image Processing, vol. 27, no. 10, pp. 4810-4824, 2018. (Paper) (Code) (A principled foreground estimation method with very effective performance!)
l Weisheng Dong, T. Huang, G. Shi, Y. Ma, and X. Li, “Robust tensor approximation with Laplacian scale mixture modeling for multiframe image and video denoising,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6, Dec. 2018. (Paper) (Code)
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, Guangming Shi, Xin Li, K. Peng, J. Wu, and Z. Guo, “Color-guided depth recovery via joint local structural and nonlocal low-rank regularization,” IEEE Trans. on Multimedia, vol. 19, no. 2, pp. 293-301, Feb. 2017. (Paper, Code)
l Y. Li, W. Dong, X. Xie, G. Shi, X. Li, and D. Xu, "Learning parametric sparse models for image super-resolution," NIPS, 2016. (Paper)
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.