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Weisheng Dong (董伟生) Professor
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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.
Selected
publication:
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 X. Lu, H. Huang, W. Dong, G. Shi, and X.
Li, “Beyond network pruning: a joint search-and-training approach,”
IJCAI, 2020. (Paper, 12% acceptance
rate！Project,
Code.)
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.