董伟生，教授，硕导、博导

硕士学科：计算机科学与技术、

                人工智能、计算机技术

                 控制科学与工程

博士学科：计算机科学与技术

工作单位：人工智能学院

1. 图像视频处理

2. 图像识别与理解

3. 深度模型轻量化

通信地址：西安电子科技大学134信箱

Email：wsdong@mail.xidian.edu.cn

办公地点：老校区主楼III区

董伟生，男，1981年4月生于浙江省兰溪市，人工智能学院教授，博导，副院长，国家级人才。主要从事图像视频处理、深度学习、计算机视觉方面的研究工作。在权威国际期刊和会议上发表论文100余篇，其中在TPAMI、IJCV、IEEE-TIP、CVPR、NeurIPS等顶级期刊和会议上发表论文60多篇。论文被引用9000余次，2篇论文单篇引用超过1400余次。曾任/现任包括国际顶级期刊IEEE Trans. on Image Processing、SIAM Journal on Imaging Sciences在内的3个期刊的编委(Associate Editor)、CVPR 2022领域主席。主持包括国家部委重大项目、国家优青、国家自然科学基金重大项目课题等项目。曾多次获得国家级青年人才称号。以第二完成人身份获2017年国家自然科学二等奖、2013年陕西省科学技术一等奖；曾获2017年陕西省自然科学论文一等奖，VCIP 2010最佳论文奖。 

招生信息：

欢迎有志青年报考我的研究生，本人每年招收硕士研究生5~8名（其中包括本校推免生，接收外校保送生名额不限），博士1-2名。硕士招生专业为国家双一流学科“计算机科学与技术”、“人工智能”、“计算机技术”。报考的同学请提前联系一下（wsdong@mail.xidian.edu.cn）。

教育经历：

• 2000.9~2004.6            华中科技大学电子信息工程系                      工学学士
• 2004.9~2010.8            西安电子科技大学电子工程学院                  工学博士

工作经历：

• 2018.1~至今               西安电子科技大学人工智能学院           教授
• 2016.7~2017.12            西安电子科技大学电子工程学院           教授
• 2012.6~2016.6              西安电子科技大学电子工程学院           副教授
• 2012.8~2013.2              微软亚洲研究院视觉计算组                 客座研究员
• 2010.9~2012.6              西安电子科技大学电子工程学院           讲师
• 2009.1~2010.6              香港理工大学计算学系                            Research Assistant

学术服务：

• IEEE Transactions on Image Processing ,  编委(Associate Editor)，07/2015~2019.7
• SIAM Journal on Imaging Science，编委(Associate Editor)，01/2017~至今
• Circuits, System and Signal Processing,  编委(Associate Editor)，2014~至今

 近期录用和发表的代表性论文：

1. Q. Ning, F. Wu, W. Dong, X. Li, and G. Shi, “Exploring Correlations in Degraded Spatial Identity Features for Blind Face Restoration,” ACM Multimedia, 2023. (CCF-A 类会议)
2. Y. Liu, T. Huang, W. Dong, X. Li, and G. Shi, “Low-Light image enhancement with multi-stage residue quantization and brightness-aware attention,” IEEE ICCV, 2023. (CCF-A 类会议)
3. J. Xu, F. Wu, X. Li, W. Dong, T. Huang, and G. Shi, “Spatially varying prior learning for blind hyperspectral image fusion,” IEEE Trans. on Image Processing, in press, 2023. (CCF-A类期刊)
4. T. Huang, W. Dong, F. Wu, X. Li, and G. Shi, “Uncertainty-driven knowledge distillation for language model compression,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, in press, 2023. 
5. C. Wang, W. Dong*, X. Li, F. Wu, J. Wu, and G. Shi, “Memory based temporal fusion network for video deblurring,” International Journal of Computer Vision, 2023. (CCF-A)
6. T. Huang, X. Yuan, W. Dong*, J. Wu, and G. Shi, “Deep Gaussian Scale Mixture Prior for Image Reconstruction,” IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 2023. (CCF-A)
7. Z. Yang, W. Dong*, X. Li, Y. Sun, M. Huang and G. Shi, “Vector Quantization with Self-attention for Quality-independent Representation Learning”, IEEE CVPR 2023. (CCF-A)
8. Z. Fang, F. Wu, W. Dong, X. Li, J. Wu and G. Shi, “Self-supervised non-uniform kernel estimation with flow-based motion prior for blind image deblurring,” IEEE CVPR 2023. (CCF-A)
9. X. Lu, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Adaptive search-and-training for robust and efficient network pruning,” IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 2023. (CCF-A)
10. L. Sun, Y. Wang, F. Wu, X. Li, W. Dong, and G. Shi, “Deep unfolding network for efficient mixed video noise removal,” IEEE Trans. on Circuits and System for Video Technology (T-CSVT), 2023. (中科院1区)
11. Q. Ning, W. Dong*, X. Li and J. Wu, “Searching efficient model-guided deep network for image denoising,” IEEE Trans. on Image Processing, vol. 23, pp. 668-681, 2023. (CCF-A)
12. Z. Fang, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Uncertainty learning in kernel estimation for multi-stage blind image super-resolution,” ECCV 2022. (计算机视觉领域顶级会议)
13. Z. Yang, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Self-feature distillation with uncertainty modeling for degraded image recognition,” ECCV 2022. (计算机视觉领域顶级会议)
14. X. Lu, T. Xi, B. Li, G. Zhang, and W. Dong, “Bayesian based re-parameterization for DNN model pruning,” ACM Multimedia, 2022. (CCF-A)
15. W. Dong, J. Wu, L. Li, G. Shi, and X. Li, “Bayesian deep learning for image reconstruction: from structured sparsity to uncertainty estimation,” IEEE Signal Processing Magazine, vol. 40, no. 1, pp. 73-84, 2023. (中科院1区)
16. Q. Ning, J. Tang, F. Wu, W. Dong*, et al., “Learning degradation Uncertainty for unsupervised real-world image super-resolution,” IJCAI 2022. (CCF-A)
17. T. Huang, W. Dong*, J. Wu, L. Li, X. Li, and Guangming Shi, “Deep hyperspectral image fusion network with iterative spatio-spectral regularization,” IEEE Trans. on Computational Imaging, 2022.
18. Y. Zhu, W. Dong*, X Li, J. Wu, L. Li, and G. Shi, “Robust depth completion with uncertainty-driven loss functions,” AAAI 2022. (CCF-A)
19. Q. Ning, W. Dong*, X. Li, J. Wu, and G. Shi, “Uncertainty-driven loss for single image super-resolution,” NeurIPS 2021. (CCF-A)
20. Y. Cao, G. Shi, T. Zhang, W. Dong*, J. Wu, X. Xie, and X. Li, “Bayesian correlation filter learning with Gaussian scale mixture model for visual tracking”, IEEE Trans. on Circuit and Systems for Video Technology (T-CSVT), 2021. (中科院1区)
21. L. Sun, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Deep maximum a posterior estimator for video denoising”, International Journal of Computer Vision (IJCV), 2021. (CCF-A)
22. W. Dong, C. Zhou, F. Wu, J. Wu, G. Shi, and X. Li, “Model-guided deep hyperspectral image super-resolution,” IEEE Trans. on Image Processing (T-IP), 2021. (CCF-A)
23. T. Huang, W. Dong*, X. Yuan*, J. Wu, and G. Shi, “Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging,” IEEE CVPR 2021. (CCF-A)
24. 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 (J-STSP), vol. 15, no. 2, pp. 240-252, Feb. 2021. (中科院1区)
25. X. Lu, H. Huang, W. Dong*, G. Shi, and X. Li, “Beyond network pruning: a joint search-and-training approach,” IJCAI, 2020. (CCF-A)
26. 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 (J-STSP), vol. 14, no. 4, pp. 817-827, May, 2020. (中科院1区)
27. Q. Ning, W. Dong*, F. Wu, J. Wu, J. Lin, and G. Shi, “Spatial-temporal Gaussian scale mixture modeling for foreground estimation,” AAAI 2020. (CCF-A)
28. 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 (T-CI), vol. 5, no. 4, pp. 635-648, 2019.
29. 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 (T-PAMI), Vol. 41, no. 10, pp. 2308-2318, Oct., 2019. (CCF-A)

Google scholar profile:   http://scholar.google.com/citations?user=-g58LsoAAAAJ&hl=en

English homepage： http://see.xidian.edu.cn/faculty/wsdong