王明华

2023年12月06日 20:59 王明华 点击:[]

基本信息

姓名:王明华

性别:

所属部门:机器人与信息自动化研究所

行政职务:

职称:副教授

学历:博士

所学专业:控制科学与工程

办公电话:

电子邮件:wangminghua@nankai.edu.cn

研究方向:人工智能算法、图像处理、机器学习、深度学习

个人简介

1)教育经历:

2012.09-2016.07,哈尔滨工业大学,自动化,学士

2016.09-2021.07,哈尔滨工业大学,控制科学与工程,硕博连读

2019.06-2020.07,   法国Grenoble INP, CSC博士联合培养,

2)工作经历:

2021.10-2023.10,中国科学院空天信息创新研究院,博士后,张兵研究员团队

2023.11-至今,南开大学,人工智能学院,副教授,赵新教授团队


github链接:https://github.com/MinghuaWang123

欢迎各位对图像处理、人工智能算法、遥感等领域感兴趣的同学随时联系我(wangminghua@nankai.edu.cn):)

科研项目、成果、获奖、专利

长期从事人工智能算法和图像处理研究,主要包括遥感图像去噪和重建、异常目标检测、跨模态智能融合。

主持项目:

国家自然科学基金青年项目(2023-2025)、天津市自然科学基金青年项目(2025-2026)

中国博士后科学基金站中特别资助(2023)、中国博士后科学基金面上项目(2022)、

遥感国家重点实验室开放基金(2024)、中国科学院特别研究助理项目(2021)

参与项目:

科技部国家重点研发项目(2项)、国家自然科学基金重点项目、面上项目

撰写论文、专著、教材等

近5年代表论文:

[11] M. Wang, et. al., "Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection," in IEEE Transactions on Cybernetics, vol. 53, no. 1, pp. 679-691, Jan. 2023. (SCI, JCR:Q1,中科院一区TOP, IF=11.8,ESI高被引).

[10] M. Wang, et. al., "Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A comprehensive review," in IEEE Geoscience and Remote Sensing Magazine, doi: 10.1109/MGRS.2022.3227063, 2023. (SCI, JCR:Q1, 中科院一区, IF=16.2ESI高被引)

[9] M. Wang*, et. al., "Hyperspectral Image Mixed Noise Removal Based on Multidirectional Low-Rank Modeling and Spatial–Spectral Total Variation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 488-507, Jan. 2021. (SCI, JCR:Q1, 中科院一区TOP, IF=8.2ESI高被引).

[8] M. Wang*, et. al., " l0-l1 Hybrid Total Variation Regularization and its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7695-7710, Sept. 2021. (SCI, JCR:Q1, 中科院一区TOP, IF=8.2).

[7] M. Wang, et. al., "Learning Double Subspace Representation for Joint Hyperspectral Anomaly Detection and Noise Removal," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3261964, 2023. (SCI, JCR:Q1, 中科院一区TOP, IF=8.2)

[6] M. Wang*, et. al., "Tensor Low-Rank Constraint and l0 Total Variation for Hyperspectral Image Mixed Noise Removal," in IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 718-733, April 2021. (SCI, JCR:Q1, 中科院一区TOP,IF=7.5).

[5] M. Wang*, et. al., "Total Variation Regularized Weighted Tensor Ring Decomposition for Missing Data Recovery in High-Dimensional Optical Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022. (SCI, JCR:Q1, 中科院三区,IF=4.8).

[4] M. Wang* and Q. Wang, "Hypergraph-regularized sparse representation for color image super resolution, " in Journal of Visual Communication and Image Representation, vol. 74, Jan. 2021. (SCI, JCR:Q2,中科院三区,IF=2.887).

[3] M. Bi, M. Wang*, Z. Li, et. al., "Vision Transformer with Contrastive Learning for Remote Sensing Image Scene Classification, " in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp:738-749, 2023. (SCI, JCR:Q2, 中科院二区TOP,IF=4.715ESI高被引).

[2] M. Wang, et. al., "Hyperspectral Anomaly Detection Using Tensor Low-Rank Representation, " 2022 10th China Conference on Command and Control. Springer, 2022: 127-132. (EI)

[1] M. Wang, et. al., "l0 Gradient Regularized Low-Rank Tensor Model for Hyperspectral Image Denoising, "2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2019: 1-6. (EI)


讲授课程

  1. 《计算机组成原理》本科生课程

  2. 《深度神经网络》研究生课程

社会兼职

电气电子工程师协会(IEEE)会员、国际数字地球学会中国国家委员会成像光谱对地观测专业委员会委员、

中国图象图形学会遥感图像专业委员会委员、中国自动化学会动态学习与智能医学专业委员会(筹)委员

Co-chair the Fusion session at IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

期刊审稿人:

IEEE Transactions on Cybernetics, IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Geoscience and Remote Sensing Letter, Journal of the Franklin Institute等



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