董伟生，男，1981年4月生于浙江省兰溪市，人工智能学院教授，副院长，入选中组部“万人计划”青年拔尖人才、教育部“青年长江学者”特聘教授，获国家优秀青年科学基金资助，入选陕西省特支计划青年科技创新领军人才，学校“华山学者”计划。主要从事深度学习、图像稀疏与低秩表示、图像视频表达与处理、计算机视觉和模式识别等领域的研究工作。发表论文80余篇，其中在TPAMI、IJCV、IEEE-TIP、IEEE-TCSVT、CVPR、ICCV、NIPS等国际权威期刊和会议上发表论文40余篇，论文被引用7100余次，2篇论文单篇引用超过1200余次，8篇论文入选 ESI 高被引论文。担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)。
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.)，进行初步面试。
教育经历：2000.9~.6 华中科技大学电子信息工程系 工学学士
.9~.8 西安电子科技大学电子工程学院 工学博士
工作经历.1~至今 西安电子科技大学人工智能学院 教授
.7~.12西安电子科技大学电子工程学院 教授
.6~.6 西安电子科技大学电子工程学院 副教授
.8~.2 微软亚洲研究院视觉计算组 客座研究员
.9~.6 西安电子科技大学电子工程学院 讲师
.1~.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/~.7
Circuits, System and Signal Processing, 编委(Associate Editor)，~至今
Reviewer for:
IEEE Trans. IP, IEEE Trans. MI, SIAM J. of Imaging Science, Optics Express,
IEEETrans.MM, IEEE Trans. SMC,IEEE Trans. CSVT,IEEE SPL, JVCIR, JEI
ICCV\'15, CVPR\'14,15,16, ECCV\'14,16, SIGGRAPH Asia\'14, 16
News!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, . (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, . (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 . (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. . (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, . (Paper, 12% acceptance rate！
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, . (,)
Q. Ning, W. Dong, F. Wu, J. Wu, J. Lin, and G. Shi, “Spatial-temporal Gaussian scale mixture modeling for foreground estimation,” AAAI . (, 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, . ()
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., .
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., .
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, .(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. .
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, .(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. . (Paper, Code)
Y. Li, W. Dong, X. Xie, G. Shi, X. Li, and D. Xu, "Learning parametric sparse models for image super-resolution," NIPS, . (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 . (Paper, Project, Code) (A very effective non-negative dictionary learning and sparse coding algorithm has been proposed!)
