西安电子科技大学人工智能学院导师教师师资介绍简介-董伟生

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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. 图像与视频处理
2. 深度网络模型结构搜索与剪枝
3. 计算机视觉

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，副院长，入选中组部“****”青年拔尖人才、教育部“青年****”****，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****”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.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：
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
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, 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 rate！Project, 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

学术论文
Book charpter:
Weisheng Dong and Xin Li, Sparsity-Regularized Image Restoration: Locality and Convexity, Image Restoration: Fundamentals and Advances, 115, CRC Press, 2018.
Xin Li, Weisheng Dong, Guangming Shi, Sparsity-Based Denoising of Photographic Images: From model-based to data driven, Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends, 2018.
Weisheng Dong, Xin Li, and Lei Zhang, “Sparsity-regularized image restoration: locality and convexity revisited,” in Image Restoration: Fundamentals and Advances, CRC Press, Bahadir Gunturk and Xin Li (Editors), 2011. (PDF)
国际期刊：
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.
J. Ma, J. Wu, L. Li, W. Dong, X. Xie, G. Shi, and W. Lin, “Blind Image Quality Assessment With Active Inference”, IEEE Trans. on Image Processing, vol. 30, no. 3, pp. 3650-3663, March 2021.
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, 240-252, 2021.
H. Zhu, L. Li, J. Wu, W. Dong, G. Shi, Generalizable No-Reference Image Quality Assessment via Deep Meta-learning, IEEE Trans. on Circuits and Systems for Video Technology, 2021.
F. Wu, T Huang, W. Dong, G. Shi, Z. Zheng, X Li, “Toward blind joint demosaicing and denoising of raw color filter array data”, Neurocomputing, 2021.
F. Wu, W. Dong, T. Huang, G. Shi, S. Cheng, X. Li, “Hybrid sparsity learning for image restoration: An iterative and trainable approach”, Signal Processing, vol. 178, 107751, Jan. 2021.
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.
J. Wu, J. Ma, F. Liang, W. Dong, G. Shi, and W. Lin, “End-to-end blind image quality prediction with cascaded deep neural network”, IEEE Trans. on Image Processing, vol. 29, pp. 7414-7426, 2020.
J. Wu, C. Ma, L. Li, W. Dong, and G. Shi, “Probabilistic Undirected Graph Based Denoising Method for Dynamic Vision Sensor”, IEEE Trans. on Multimedia, 2020.
J. Wu, W. Yang, L. Li, W. Dong, G. Shi, and W. Lin, “Blind image quality prediction with hierarchical feature aggregation”, Information Sciences, vol. 552, pp. 167-182, 2020.
Weisheng Dong, Peiyao Wang, Wotao Yin, Guangming Shi, Fangfang Wu, Xiaotong Lu, Denoising Prior Driven Deep Neural Network for Image Restoration, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , Oct. 2019.
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, in press, 2019.
J. Wu, Y. Liu, W. Dong, G. Shi, and W. Lin, “Quality assessment for video with degradation along salient trajectories”, IEEE Trans. on Multimedia, vol. 21, no. 11, pp. 2738-2749, 2019.
J. Song, X. Xie, G. Shi, and W. Dong, “Multi-layer discriminative dictionary learning with locality constraint for image classification”, Pattern Recognition, vol. 91, pp. 135-146, 2019.
J. Wu, M. Zhang, L. Li, W. Dong, G. Shi, and W. Lin, “No-reference image quality assessment with visual pattern degradation”, Information Sciences, vol. 504, pp. 487-500, 2019.
X. He, B. Shi, X. Bai, G. Xia, Z. Zhang, W Dong, Image caption generation with part of speech guidance, Pattern Recognition Letters, vol. 119, pp. 229-237, 2019.
J. Wu, J. Zeng, W. Dong, G. Shi, W. Lin, Blind image quality assessment with hierarchy: Degradation from local structure to deep semantics, Journal of Visual Communication and Image Representation, vol. 58, pp. 353-362, 2019.
Y. Zhou, L. Li, J. Wu, K. Gu, W. Dong, and G. Shi, “Blind quality index for multiply distorted images using biorder structure degradation and nonlocal statistics”, IEEE Trans. on Multimedia, vol. 20, no. 11, pp. 3019-3032, 2018.
Weisheng Dong, Tao Huang, Guangming Shi, Yi Ma, and Xin Li, “Robust Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image and Video Denoising”, IEEE Journal of Selected Topics in Signal Processing (JSTSP), vol. 12, no. 6, 1435-1448, 2018.
Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Jinjian Wu, and Xin Li, “Image super-resolution with parametric sparse model learning”, IEEE Transactions on Image Processing (TIP), vol. 27, no. 9, pp. 4638-4650, 2018.
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.
Tao Huang, Weisheng Dong, X. Xie, et al. “Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-rank Approximation,” IEEE Transactions on Image Processing (TIP), vol. 26, no. 7, pp.3171-3186, 2017.
Weisheng Dong, Guangming Shi, Xin Li, Jinjian Wu, and Zhenhua Guo, “Color-guided depth recovery via local structural and nonlocal low-rank regularization,” IEEE Transactions on Multimedia, vol. 19, no. 2, pp. 293-301, 2017.
Jinjian Wu, Leida Li, Weisheng Dong, Guangming Shi, Weisi Lin, C.-C. Jay Kuo, “Enhanced Just Noticeable Difference Model for Images With Pattern Complexity”, IEEE Trans. on Image Processing, vol. 26, no. 6, pp. 2682-2693, June, 2017.
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!)
Weisheng Dong, Guangming Shi, Yi Ma, and Xin 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!).
Weisheng Dong, Xiaolin Wu, and Guangming Shi, "Sparsity fine tuning in Wavelet domain with application to compressive image reconstruction", IEEE Trans. on Image Processing (TIP), vol. 23, no. 12, pp. 5249-5262, Dec. 2014. (PDF) (Code coming soon)
Weisheng Dong, Guangming Shi, Xiaocheng Hu, and Yi Ma, "Nonlocal sparse and low-rank regularization for optical flow estimation," IEEE Trans. on Image Processing (TIP),vol. 23, no. 10, pp. 4527-4538, 2014. (PDF) (Code)
Weisheng Dong, Guangming Shi, Xin Li, Yi Ma, and Feng Huang, "Comressive sensing via nonlocal low-rank regularization", IEEE Trans. on Image Processing (TIP), vol. 23, no. 8, pp. 3618-3612, Aug. 2014. (PDF) (Code & Project) (State-of-the-art CS reconstruction performance on both natural images and complex-valued MRI images!)
Weisheng Dong, Lei Zhang, Guangming Shi, and Xin Li, “Nonlocally centralized sparse representation for image restoration,” IEEE Trans. on Image Processing (TIP), vol. 22, no. 4, pp. 1620-1630, Apr. 2013. (PDF) (Code) (Excellent image denoising performance!)
Weisheng Dong, Lei Zhang, Rastislav Lukac, and Guangming Shi, “Sparse representation based image interpolation with nonlocal autoregressive modeling,” IEEE Trans. on Image Processing (TIP), vol. 22, no. 4, pp. 1382-1394, Apr. 2013. (PDF) (Code)
Weisheng Dong, Guangming Shi, and Xin Li, “Nonlocal image restoration with bilateral variance estimation: a low-rank approach,” IEEE Trans. on Image Processing (TIP), vol. 22, no. 2, pp. 700-711, Feb. 2013. (PDF) (Code)
Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. on Image Processing (TIP), vol. 20, no. 7, pp. 1838-1857, July 2011. (PDF) (Code)
Xiaolin Wu, Weisheng Dong, Xiangjun Zhang, and Guangming Shi, “Model-assisted adaptive recovery of compressed sensing with imaging applications,” IEEE Trans. on Image Processing (TIP), vol. 21, no. 2, Feb. 2012. (PDF)
Weisheng Dong, Guangming Shi, Xiaolin Wu, Lei Zhang, “A learning-based method for compressive image recovery,” Journal of Visual Communication and Image Representation, vol. 24, no. 7, pp. 1055-1063, 2013.
Weisheng Dong, Xiafang Yang, and Guangming Shi, “Compressive sensing via reweighted TV and nonlocal sparsity regularisation”, Electronic Letters, vol. 49, no. 3, pp. 184-186, 2013.
Weisheng Dong, Guangming Shi, Xin Li, Lei Zhang, and Xiaolin Wu, “Image reconstruction with locally adaptive sparsity and nonlocal robust regularization,” Signal Processing: Image Communication, vol. 27, pp. 1109-1122, 2012. (PDF)
Lei Zhang, Weisheng Dong, Xiaolin Wu, and Guangming Shi “Spatial-temporal color video reproduction from noisy CFA sequence,” IEEE Trans. On Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 838-847, June 2010. (PDF)
Lei Zhang, Weisheng Dong, David Zhang, Guangming Shi, “Two-stage Image Denoising by Principle Component Analysis with Local Pixel Grouping”, Pattern Recognition, vol. 43, pp. 1531-1549, Apr. 2010. (PDF) (Code)
Weisheng Dong, Guangming Shi, and Jizheng Xu, “Adaptive nonseparable interpolation for image compression with directional wavelet transform,” IEEE Signal Processing Letters, vol. 15, pp. 233-236, 2008. (PDF)
Guangming Shi, Weisheng Dong, Xiaolin Wu, and Lei Zhang, “Context-based adaptive image resolution upconversion,” Journal of Electronic Imaging, vol. 19, 013008, 2010. (PDF)
Weisheng Dong, Guangming Shi, and Li Zhang, “Immune Memory clonal selection algorithms for designing stack filters,” Neurocomputing, pp. 777-784, Jan. 2007.

国际会议：
T. Huang, W. Dong, X. Yuan, J. Wu, and G. Shi, “Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging,” IEEE CVPR 2021.
X. Lu, H. Huang, W. Dong, G. Shi, and X. Li, “Beyond network pruning: a joint search-and-training approach,” IJCAI, 2020.
H. Zhu, L. Li, J. Wu, W. Dong, G. Shi, “MetaIQA: deep meta-learning for no-reference image quality assessment”, CVPR, 2020.
Q. Ning, W. Dong, F. Wu, J. Wu, J. Lin, and G. Shi, “Spatial-temporal Gaussian scale mixture modeling for foreground estimation,” AAAI 2020.
J. Ma, J. Wu, L. Li, W. Dong, X. Xie, “Active Inference of GAN for No-Reference Image Quality Assessment”, IEEE International Conference on Multimedia and Expo (ICME), 2020.
J. Wu, J. Ma, F. Liang, W. Dong, G. Shi, “End-to-End Blind Image Quality Assessment with Cascaded Deep Features”, IEEE International Conference on Multimedia and Expo (ICME), pp. 1858-1863, 2019.
F. Wu, Y. Li, J. Han, W. Dong, G Shi, “Perceptual Image Dehazing Based on Generative Adversarial Learning”, Pacific Rim Conference on Multimedia, pp. 877-887, 2018.
T. Huang, F. Wu, W. Dong, G. Shi, X Li, “Lightweight deep residue learning for joint color image demosaicking and denoising”, IEEE International Conference on Pattern Recognition (ICPR), pp. 127-132, 2018.
W. Wan, J. Wu, G. Shi, Y. Li, W. Dong, “Super-resolution quality assessment: Subjective evaluation database and quality index based on perceptual structure measurement”, IEEE International Conference on Multimedia and Expo (ICME), 2018.
Y. Li, W. Dong, X. Xie, G. Shi, X. Li, and D. Xu, "Learning parametric sparse models for image super-resolution," NIPS, 2016.
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)
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)
Weisheng Dong, Xin Li, Yi Ma, an Guangming Shi, "Image reconstruction via Bayesian Structured Sparse Coding", IEEE Int. Conf. on Image Processing, 2014. (Oral)
Weisheng Dong, Xiaolin Wu, and Guangming Shi, "Sparsity fine tuning in Wavelet domain with application to compressive image reconstruction", IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
Weisheng Dong, Guangming Shi, and Xin Li, “Image deblurring with low-rank approximation structured sparse representation,” APSIPA, 2012. (Invited paper) (PDF)
Weisheng Dong, Xin Li, Lei Zhang, and Guangming Shi, “Sparsity-based image denoising via dictionary learning and structure clustering,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 457-464, 2011. (PDF), (code) (Oral presentation, acceptance rate: 3.5%=59/1677)
Weisheng Dong, Lei Zhang, and Guangming Shi, “Centralized Sparse Representation for Image Restoration,” in Proc. IEEE Int. Conf. on Computer Vision (ICCV), Barcelona, Spain, 2011. (PDF) (Code)
Weisheng Dong, Guangming Shi, Lei Zhang, and Xiaolin Wu, “Super-resolution with nonlocal regularized sparse representation,” in Proc. SPIE Visual Communications and Image Processing (VCIP), July 2010. (PDF) (Best Paper Award)
Weisheng Dong, Xin Li, Lei Zhang, and Guangming Shi, “Sparsity-based image deblurring with locally adaptive and nonlocally robust regularization,” accept to Proc. IEEE International Conference on Image Processing (ICIP), 2011. (PDF)
Weisheng Dong, Xiaolin Wu, Guangming Shi, and Lei Zhang, “Context-based bias removal of statistical models of wavelet coefficients for image denoising,” in Proc. IEEE International Conference on Image Processing (ICIP), Oct. 2009.
Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, “Nonlocal back-projection for adaptive image enlargement,” in Proc. IEEE International Conference on Image Processing (ICIP), Oct. 2009. (PDF) (Code)
Fangfang Wu, Guangming Shi, Weisheng Dong, and Xiaolin Wu, “Learning-based recovery of compressive sensing with application in multiple description coding,” in Proc. IEEE International Workshop on Multimedia Signal Processing (MMSP), Oct. 2009.
Weisheng Dong, Guangming Shi, and Jizheng Xu, “Signal-adapted directional lifting scheme for image compression,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1392-1395, 2008.
Guangming Shi, Weisheng Dong, and Li Zhang, “A new fast algorithm for training large window stack filters,” in Proc. International Conference on Natural Computation (ICNC), pp. 724-733, 2006.
Guangming Shi, Weisheng Dong, “The design and implementation of stack filter based on immune memory clonal algorithms with hybrid computation,” in Proc. IEEE International Midwest Symposium on Circuits and Systems (IMSCS), pp. 7-10, Aug. 2005.

课程教学
目前本人承担本科教学任务：
春季：《数字多媒体技术》
秋季：《微机原理》
课件下载示例

科研项目
国家自然科学基金委重大项目课题，2020-2024
科技部重点研发项目课题，2019-2022
华为联合项目，2019-2020
学校“三个一流”建设项目
国家自然科学基金优秀青年科学基金，2017-2019
国家自然科学基金面上项目，2015~2018
陕西省青年科技新星项目，2014~2015
基本科研业务费大数据群项目，2014~2015
国家自然科学基金青年基金，2012~2014

2017年国家自然科学二等奖（排名第二）
2017年陕西省自然科学论文一等奖（第一作者）
2014 IEEE Conf. on Image Processing (ICIP), Top 10% Paper Award
2013年陕西省青年科技新星
2013年陕西省科学技术一等奖（排名第二）
2013年陕西省优秀博士论文
2012 微软青年****铸星计划
2010 SPIE/IEEE Vis. Comm. and Image Proc.(VCIP) conference, Best Paper Award
2010年德国纽伦堡国际发明博览会银奖

招生要求
~~~~~~~~~~~~~~~~~~~~~~~~~~
硕士&博士研究生招生信息
~~~~~~~~~~~~~~~~~~~~~~~~~~
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，进行初步面试。满足以下条件者优先考虑：
(1) 具有较好的编程能力(Python、C/C++)者；
(2) 本科期间参考过学科竞赛，有较强动手实践能力；
(3) 对人工智能算法研究兴趣浓厚者。
请通过邮件：wsdong@mail.xidian.edu.cn 与我联系。近5年毕业学生去向：
刘金山，2021届硕士，现就职于快手；
张松林，2021届硕士，现就职于京东；
周晨，2021届硕士，现就职于爱奇艺；
黄晗，2021届硕士，现就职于腾讯；
袁鹏，2020届硕士，现就职于海康威视；
王欢，2020届硕士，现就职于华为；
王亨，联合指导2020届硕士，现就职于旷世；
王佩瑶，2019届硕士，UniversityatBuffalo 读博;
张珍，联合指导2019届硕士，现就职于迈瑞医疗；
范兴宣，2019届硕士，现就职于阿里巴巴；
李永波，联合指导2018届博士，现就职于阿里巴巴；
黄韬，联合指导2018届博士，现就职于阿里巴巴；
韩建稳，2018届硕士，现就职于四维图新；
杨文慧，2018届硕士，现就职于VIPKID；
袁明，2018届硕士，现就职于美团点评；
蒋剑锋，联合指导2018届硕士，现就职于大疆；
严章熙，联合指导2018届硕士，现就职于滴滴出行；
楼佳珍，联合指导2018届硕士，现就职于阿里巴巴；
流水，2017届硕士，现就职于中电20所；
巨丹，2017届硕士，现就职于VIVO；
傅发佐，2017届硕士，现就职于链家；
高海涛，联合指导2017届硕士，现就职于滴滴出行。

新增栏目0

2015年中国电子学会
第二十一届青年学术年会
Twenty-first Youth Annual Conference
征稿通知

西安电子科技大学
2015年7月
中国电子学会第二十一届青年学术年会（简称：CIE-YC2015）将于2015年9月下旬在古城西安召开。中国电子学会青年学术年会是电子信息技术界青年科技工作者一年一度交流、沟通、合作、创新的盛会，已经成为电子和信息技术领域的青年科研人员探讨新技术、新思想的平台。结合国家“十三五规划”及《国家中长期科学技术发展规划纲要》，“加强军民科技资源融合，推动交叉学科和新兴学科发展，提升核心技术创新与多学科集成”是本次年会的主题。本次会议将就天线、通信、测控、计算机、控制科学、电子科学与技术领域的最新进展、发展趋势以及在我国高新技术产业中的应用展开讨论，并邀请国内著名专家****进行最新学术前沿报告。
1. 征文范围
会议现在开始征文（中英文均可），所有录用论文将编入年会论文集中。会议评选的优秀论文推荐至2015 IET International Symposium on Mechatronics (EI检索)、西安电子科技大学学报（EI检索）。欢迎大家踊跃投稿并参加会议，征文内容如下：
1) 天线理论与工程：包括相控阵天线、可重构天线、毫米波天线、共形天线、可展开天线、反射面天线、频率选择表面、天线罩、微带天线、多频段/宽带天线、瞬态天线、天线测量等。
2) 通信与电子科学：包括通信理论、无线/移动通信与技术、多媒体处理、网络安全、移动互联网、信息技术与应用、电路与系统、电子材料与元器件等。
3) 计算机科学：包括计算机科学理论、体系结构、计算机网络、并行与分布式处理、软件工程与软件方法学、信息安全、虚拟现实、人工智能、模式识别与机器学习、生物信息处理等。
4) 控制科学与工程：包括控制理论、故障诊断与系统维护、系统仿真与评估、导航/制导与测控、传感器与传感器网络、多源信息融合等。
5) 电子机械：包括电子机械科学与技术、微波天线机电耦合理论与方法、现代雷达机械设计、空间可展开天线设计与制造、机器人与机械手、电子封装、精密测控等。
2. 专题研讨会
1) 机器人技术：围绕机器人与智能社会、机器人控制、多传感器集成与信息融合、微型和微小机器人、海洋机器人、医用机器人、机器人执行机构设计等领域展开研究与探讨。
2) 空间太阳能电站(SSPA)技术：面向空间太阳能电站系统，围绕太阳能收集与转换技术、大功率微波传输技术以及地面接收转化技术等方面开展研究与探讨。
3) 空间信息网络：面向信息网络学科发展前沿和国家发展空间信息网络的重大急需，针对空间动态立体组网、高速传输、信息融合处理和飞行器控制等方面开展研讨，探索空间网络理论、网络信息论和空间信息学等前沿理论，围绕动态网络拓扑优化、高速信息跟瞄传输及多维数据融合应用等技术难题进行探讨。
4) 北斗卫星导航与产业应用：根据北斗系统提供的定位、导航、授时、短报文等服务，从中国北斗卫星导航系统的应用和发展的高度，围绕北斗系统应用设备开发、北斗核心关键元器件的研发、应用解决方案、运营管理以及政策研究等方面问题展开讨论。
5) 新一代通信技术：围绕光通信技术、无线通信技术及现代服务业与宽带通信业务等领域进行研究与探讨。
3. 投稿事宜
1) 要求内容详实、新颖，具有学术交流价值，且未在国内外公开发行的刊物或会议上发表或宣读过，严禁抄袭和造假。
2) 论文绝对不得涉及保密内容，文责自负。
3) 每篇论文一般介绍前三位作者，包括作者的性别、出生年月、籍贯、职称、主要研究方向、电子邮箱，附在论文后面。第一作者年龄不超过40岁。
4) 投稿稿件请用Word排版，论文格式及排版要求详见附件。
5) 会议录用的论文在发表前必须与作者签署版权转让协议。
4. 重要说明
（1）相关时间：
论文投稿截稿：2015年8月20日
录用通知发放：2015年9月5日
论文最终版提交：2015年9月12日
（2）投稿方式：
本届会议将采用E-mail投稿方式，邮箱地址为“cie21youth@163.com”，其中应附有两个附件，均以word文档形式上传，内容分别是：
附件A、投稿论文的正文；
中英文稿件均接收，论文格式请按照模板进行排版。
附件B、将以下内容填写完整，在word内制表；
编号
内容方向
作者
论文名称
工作单位
通讯地址
邮编
联系电话
Email
其它

其中，一篇论文对应一个“编号”；“内容方向”请根据征稿通知的5个方向选择其一；“作者”应为论文的所有作者名称，并按照第一、二顺序排列。
（3）费用说明：
大会免会议注册费和论文版面费，对其中的优秀论文将颁发证书。
5. 会议地点
陕西省西安市
6. 联系方式
联系人：肖岚
电子邮件：cie21youth@163.com
7.
主办单位：中国电子学会
承办单位：中国电子学会青年工作委员会，陕西省电子学会
西安电子科技大学

附件
投稿格式说明

(1) 论文要求主题明确、数据可靠、逻辑严谨、文字精炼。文稿必须包括中文题名、中文作者姓名和作者汉语拼音姓名、作者中文工作单位和邮编、中英文摘要和中文关键词、中图分类号（可查阅《中国图书分类法》）、正文和参考文献。
(2) 论文篇幅（含图表）限8000字（正文采用单栏排版，A4版面）。来稿要求需采用Word 2003以上版本，并通过网上提交。页边距采用Word默认页边距或调整为：上下边距2.5cm，左右边距3cm。正文统一采用五号字，1.5倍行距。
(3) 论文题名应恰当反映文章的特定内容，一般不用副标题。中文题名一般不超过20个汉字，字体为小二宋加粗居中。
(4) 论文摘要应写成报道性摘要，包括目的、方法、结果、结论4部分。中文摘要以200～250个汉字为宜，字体为五号宋体；英文摘要字体为五号Times New Roman。
(5) 关键词应从题名、摘要或主题内容中抽取，选用能反映文章特征的、通用性强的、为同行所熟知的单词和组合词作为关键词。每篇论文选取3～8个词作为关键词，字体为五号宋体加粗。
(6) 文中图表应具有自明性，且随文出现。图中文字、符号、纵横坐标中的标值、标值线必须清楚，标目应使用标准的物理量和单位符号。表的内容切忌与图和文字内容重复。
(7) 正文(含图表)中的物理量和计量单位必须符合国家标准和国际标准。外文字母的文种、字体的大小写、上下角标及易混的字母应书写清楚，必要时需特殊作出标注。
(8) 论文的结论应是最终的、总体的结论，决不是正文中各段小结的简单重复。论文的结论应从五方面来写：由对研究对象进行的考察或实验得到的结果所揭示的原理及其普遍性；研究中有无发现例外或本论文尚难以解释、难以解决的问题；与先前已发表过的研究工作的异同；本论文在理论上和实用上的意义与价值；对进一步深入研究本课题的建议。
(9) 参考文献选用主要的、公开发表的文献。参考文献表采用顺序编码制，按文中出现的先后顺序编号。几种常见文献的著录格式如下：
专著：［序号］著者.书名［M］.出版城市名：出版单位，出版年. 起止页码.
期刊：［序号］作者.题名［J］.刊名，出版年，卷(期)：起止页码.
学位论文：［序号］作者.题名［D］.保存城市名：保存单位(系级)，授予年.
论文集：［序号］作者.题名［A］.编者.论文集名［C］.出版城市名：出版单位，出版年. 起止页码.
专利：［序号］专利所有者.题名［P］.专利国别：专利号，出版日期.
标准：［序号］标准号，标准名［S］.

Profile
Name Title
Department:

Contact Information
Address:
Email:
Tel:

Introduction
Put brief introduction of yourself here

Research Interests
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Research
目前研究团队承担的科研项目：

Papers
[1]
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Honors
点击网页顶部“添加栏目”可以添加其他栏目
把鼠标放在栏目标题处，尝试拖动栏目。

Team
团队教师

博士研究生
硕士研究生

Teaching
目前本人承担的教学任务：

课件下载示例

Admission
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关于研究生招生的信息：
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