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基本信息

董伟生,教授,硕导、博导

 

硕士学科:计算机科学与技术/

                  控制科学与工程

                  电子通信工程 

博士学科:计算机科学与技术

工作单位:人工智能学院

主要研究方向

1. 图像生成、图像视频处理

2. 图像目标识别与理解

3. 深度模型剪枝

 

联系方式

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

个人简介

      

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,国家级领军人才。主要从事图像视频处理、深度学习,模式识别与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、ICCV、NIPS等顶级期刊和会议上发表论文50多篇。论文被引用8000余次,2篇论文单篇引用超过1300余次,8篇论文入选 ESI 高被引论文。曾任/现任包括国际顶级期刊IEEE Transactions 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、国家自然科学基金优青、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5~7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”、“电子与通信工程”。报考的同学最好报考前联系一下(wsdong@mail.xidian.edu.cn),进行初步面试。

 

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:

 

News!

  • 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. (Paper, project & code) (A novel kernel estimation method was proposed with uncertainty learning, achieving SOTA blind image SR results.)
  • 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. (Paper, project & code) (A weighted feature distillation loss with uncertainty learning was proposed for degraded image recognition.)
  • X. Lu, T. Xi, B. Li, G. Zhang, and W. Dong, “Bayesian based re-parameterization for DNN model pruning,” ACM Multimedia, 2022.
  • 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, in press, 2022.
  • Q. Ning, J. Tang, F. Wu, W. Dong*, et al., “Learning degradation Uncertainty for unsupervised real-world image super-resolution,” IJCAI 2022. (Paper, project & code)
  • 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, in press, 2022. (Paper, project & code)
  • Y. Zhu, W. Dong, X Li, J. Wu, L. Li, and G. Shi, “Robust depth completion with uncertainty-driven loss functions,” AAAI, 2022. (Paper, project & code)
  • Q. Ning, W. Dong*, X. Li, J. Wu, and G. Shi, “Uncertainty-driven loss for single image super-resolution,” NeurIPS 2021. (Paper, project & code)
  • 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), in press, 2021. (Paper, Project & Code)
  • 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), accepted, 2021. (Paper, Project & Code) (MAP-based video denoising algorithm was unfolded into a deep network, leading to principle and state-of-the-art video denoising performance!)
  • 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, in press, 2021. (Paper, Project & Code) (A model-guided DCNN was proposed for hyperspectral image super-resolution, obtaining state-of-the-art performance!)
  • 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!)
  • 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!)
  • 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 rateProject, Code.)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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!)
  • Weisheng Dong, T. Huang, G. Shi, Y. Ma, and X. Li, “Robust tensor approximation with Laplacian scale mixture modeling for multif[ant]rame image and video denoising,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6, Dec. 2018. (Paper) (Code)
  • 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!)
  • 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)
  • Y. Li, W. Dong, X. Xie, G. Shi, X. Li, and D. Xu, "Learning parametric sparse models for image super-resolution," NIPS, 2016. (Paper)
  • 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!) 

 

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

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