梅晓光 副教授

梅晓光,博士,武汉大学电子信息学院副教授,硕士生导师。2007年本科毕业于华中科技大学,获通信工程学士学位;2011年硕士毕业于华中师范大学,获通信与信息系统硕士学位,随后于中船重工722研究所从事舰船通信系统研制工作;2016年博士毕业于华中科技大学,获电路与系统博士学位,期间荣获“博士研究生国家奖学金”。2016年进入武汉大学电子信息学院,从事博士后研究工作,出站后留校晋升为副教授。主要从事高/超光谱应用中的计算机视觉、模式识别、机器学习等问题的研究,目前已在包括IEEE TIP/TGRS/GRSL/J-STARS、Information Sciences等在内的期刊和会议上发表论文40余篇,ESI高被引论文3篇,授权国家发明专利7项。主持了包括军委科技委前沿创新、国家自然科学基金、装备预研教育部联合基金青年人才、中国博士后科学基金等多项科研项目。获中国自动化学会自然科学二等奖。担任IEEE TGRS/GRSL/ACCESS/J-STARS/TCI、Information Sciences、Infrared Physics & Technology等重要期刊审稿人。(English Portal)(Google Scholar)

教育背景
  • 2012.9-2016.3 华中科技大学 电路与系统  博士
  • 2008.9-2011.6 华中师范大学 通信与信息系统  硕士
  • 2003.9-2007.6 华中科技大学 通信工程  学士
工作经历
  • 2020.3-至今 武汉大学 电子信息学院 副教授
  • 2019.5-2020.2 武汉大学 电子信息学院 讲师
  • 2016.5–2019.4 武汉大学 电子信息学院  博士后
  • 2010.10-2012.7 中船重工第七二二研究所  嵌入式软件工程师
荣誉奖励
  • 2015年博士研究生国家奖学金
  • 2017年中国自动化学会自然科学二等奖
  • 2017年Infrared Physics & Technology期刊“审稿杰出贡献”称号
科研项目

主持

  1. 2020.01-2022.12,国家自然科学基金青年项目,61903279,面向亚像素级高光谱海洋溢油探测的端元变异鲁棒解混方法研究,25万
  2. 2018.10-2020.12,军委科技委前沿创新项目,18-163-12-ZT-004-015-01, **感知技术研究,100万
  3. 2018.09-2019.10,军委科技委前沿创新项目,**目标探测与识别技术,40万
  4. 2018.01-2019.12,装备预研教育部联合基金青年人才项目,无人机红外-可见光融合图像超分辨率对地目标探测技术,80万
  5. 2017.11-2019.10,中国博士后科学基金,2017M612504,基于贝叶斯网络的高光谱盲解混方法研究,5万
  6. 2017.07-2018.06,军委科技委前沿创新项目,17-163-12-ZT-004-076-01,仿生跳跃机器人三维视觉感知技术研究,2017-2018,50万
  7. 2017.01-2018.12,中央高校自主科研项目,OP/FT-IR温室气体定量快速反演方法研究,15万

参与

  1. 2017-2019,“十三五”装备预研领域基金,高性能红外-可见光融合探测技术,50万
  2. 2017-2019,军委科技委前沿创新计划,****目标多波段实时融合探测技术,400万
  3. 2017-2018,军委科技委前沿创新计划,****尾迹多阵列红外探测,50万
  4. 2017-2018,智能机器人与系统高精尖创新中心开放基金,可见光/红外图像融合的智能机器人感知系统,15万
  5. 2016-2018,装备预研教育部联合基金,可见光/红外图像融合的****系统,100万
  6. 2016-2018,“十三五”装备预研领域基金,一种基于红外超光谱谱指纹检测的新型光电探测感知技术,50万
  7. 2016-2020,“十三五”装备预研共用技术,红外****模块技术,260万
  8. 2015-2016,863计划项目,天基低轨远程空间****方法研究,30万
  9. 2013-2016,国家自然科学基金面上项目,红外超光谱谱指纹检测和识别海洋溢油污染方法研究,75万
发明专利
  1. 一种基于边界投影最优梯度的高光谱非线性解混方法,CN201510700049.2,发明专利,2018.3.2
  2. 基于局部线性迁移和刚性模型的图像特征匹配方法及系统,CN201510807463.3,发明专利,2018.12.14
  3. 基于局部线性迁移的非刚性变换图像特征匹配方法及系统,CN201510801246.3,发明专利,2018.12.14
  4. 一种基于鲁棒低秩张量的高光谱图像去噪方法,CN201510521057.0,发明专利,2017.11.28
  5. 用于目标识别的红外超光谱信号处理方法、处理机及系统,CN201410700672.3,发明专利,2016.8.31
  6. 基于局部线性迁移和仿射变换的图像特征匹配方法及系统,CN201510799813.6,发明专利,2018.9.21
  7. 基于特征导向GMM和边缘图像的图像配准方法及系统,CN201610201334.4,发明专利,2018.10.26
学术服务

担任以下期刊审稿人或客座编辑工作:

  • IEEE Transactions on Geoscience and Remote Sensing(SCI,中科院二区,IF=5.855)
  • IEEE Transactions on Computational Imaging
  • IEEE Journal of Selected Topics in Signal Processing(SCI,中科院一区,IF=4.981)
  • IEEE J-STARS(SCI,中科院二区,IF=3.827)
  • IEEE Geoscience and Remote Sensing Letters(SCI,中科院二区,IF=3.833)
  • IEEE Access(SCI,中科院二区,IF=3.745)
  • Information Sciences(中科院一区,IF=5.91)
  • Infrared Physics & Technology(SCI,中科院三区,IF=2.379)
  • Sensors, Special Issue “Research and Application of Robust Hyperspectral Image”, Guest Editor(SCI,中科院三区,IF=3.275)Call for paper
学术论文

期刊文章:

  1. Y. Zhang, Y. Ma, X. Dai, H. Li, X. Mei*, and J. Ma, “Locality-constrained sparse representation for hyperspectral image classification,” Information Sciences, vol. 546, pp. 850–870, 2021. (SCI, 中科院一区, 2019 IF=5.91) [code]
  2. J. Chen, X. Li, L. Luo, X. Mei*, and J. Ma, “Infrared and visible image fusion based on target-enhanced multiscale transform decomposition,” Information Sciences, vol. 508, pp. 64–78, 2020. (SCI, 中科院一区, 2019 IF=5.91, 入选ESI高被引文章) [code]
  3. E. Pan, X. Mei, Q. Wang, Y. Ma, and J. Ma, “Spectral-spatial classification for hyperspectral image based on a single GRU,” Neurocomputing, vol. 387, pp. 150–160, 2020. (SCI, 中科院二区, 2019 IF=4.438)
  4. J. Ma, H. Xu, J. Jiang, X. Mei*, and X. Zhang, “DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion,” IEEE Transactions on Image Processing, vol. 29, pp. 4980–4995, 2020. (SCI, 中科院一区, 2019 IF=9.34, 入选ESI高被引文章) [code]
  5. J. Huang, Z. Le, Y. Ma, X. Mei, and F. Fan, “A generative adversarial network with adaptive constraints for multi-focus image fusion,” NEURAL COMPUTING \& APPLICATIONS, 2020. (SCI, 中科院二区,2019 IF=4.774) [code]
  6. Y. Wang, X. Mei, Y. Ma, J. Huang, F. Fan, and J. Ma, “Learning to find reliable correspondences with local neighborhood consensus,” Neurocomputing, 2020. (SCI, 中科院二区, 2019 IF=4.438)
  7. M. Wu, Y. Ma, F. Fan, X. Mei, and J. Huang, “Infrared and visible image fusion via joint convolutional sparse representation,” JOSA A, vol. 37, pp. 1105–1115, 2020. (SCI, 中科院三区,2019 IF=1.791)
  8. Y. Zhang, Z. Wan, X. Jiang, and X. Mei*, “Automatic Stitching for Hyperspectral Images Using Robust Feature Matching and Elastic Warp,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3145–3154, 2020. (SCI, 中科院二区, 2019 IF=3.827) [code]
  9. Y. Ma, G. Fan, Q. Jin, J. Huang, X. Mei, and J. Ma, “Hyperspectral Anomaly Detection via Integration of Feature Extraction and Background Purification,” IEEE Geoscience and Remote Sensing Letters, 2020. (SCI, 中科院二区, 2019 IF=3.833) [code]
  10. J. Wang, J. Chen, H. Xu, S. Zhang, X. Mei*, J. Huang, and J. Ma, “Gaussian field estimator with manifold regularization for retinal image registration,” Signal Processing, vol. 157, pp. 225–235, 2019. (SCI, 中科院二区, 2019 IF=4.384) [code]
  11. Y. Ma, Y. Wang, X. Mei, C. Liu, X. Dai, F. Fan, and J. Huang, “Visible/infrared combined 3D reconstruction scheme based on nonrigid registration of multi-modality images with mixed features,” IEEE Access, vol. 7, pp. 19199–19211, 2019. (SCI, 中科院二区, 2019 IF=3.745)
  12. Y. Ma, Q. Jin, X. Mei*, X. Dai, F. Fan, H. Li, and J. Huang, “Hyperspectral unmixing with Gaussian mixture model and low-rank representation,” Remote Sensing, vol. 11, p. 911, 2019. (SCI, 中科院二区, 2019 IF=4.509) [code]
  13. X. Mei, E. Pan, Y. Ma, X. Dai, J. Huang, F. Fan, Q. Du, H. Zheng, and J. Ma, “Spectral-spatial attention networks for hyperspectral image classification,” Remote Sensing, vol. 11, p. 963, 2019. (SCI, 中科院二区, 2019 IF=4.509, 入选ESI高被引文章) [code]
  14. Q. Jin, Y. Ma, E. Pan, F. Fan, J. Huang, H. Li, C. Sui, and X. Mei*, “Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity,” Remote Sensing, vol. 11, p. 2434, 2019. (SCI, 中科院二区, 2019 IF=4.509) [code]
  15. C. Sui, C. Li, J. Feng, and X. Mei, “Unsupervised manifold-preserving and weakly redundant band selection method for hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, pp. 1156–1170, 2019. (SCI, 中科院二区, 2019 IF=5.855)
  16. Y. Ma, Y. Zhang, X. Mei*, X. Dai, and J. Ma, “Multifeature-Based Discriminative Label Consistent K-SVD for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, pp. 4995–5008, 2019. (SCI, 中科院二区, 2019 IF=3.827)
  17. J. Ma, X. Wang, Y. He, X. Mei, and J. Zhao, “Line-Based Stereo SLAM by Junction Matching and Vanishing Point Alignment,” IEEE Access, vol. 7, pp. 181800–181811, 2019. (SCI, 中科院二区, 2019 IF=3.745)
  18. X. Mei, Y. Ma, C. Li, F. Fan, J. Huang, and J. Ma, “Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation,” Neurocomputing, vol. 275, pp. 2783–2797, 2018. (SCI, 中科院二区, 2019 IF=4.438) [code]
  19. Y. Ma, C. Li, H. Li, X. Mei, and J. Ma, “Hyperspectral image classification with discriminative kernel collaborative representation and Tikhonov regularization,” IEEE Geoscience and Remote Sensing Letters, vol. 15, pp. 587–591, 2018. (SCI, 中科院二区, 2019 IF=3.833)
  20. Q. Du, A. Fan, Y. Ma, F. Fan, J. Huang, and X. Mei*, “Infrared and Visible Image Registration Based on Scale-Invariant PIIFD Feature and Locality Preserving Matching,” IEEE Access, vol. 6, pp. 64107–64121, 2018. (SCI, 中科院二区, 2019 IF=3.745)
  21. F. Fan, Y. Ma, C. Li, X. Mei, J. Huang, and J. Ma, “Hyperspectral image denoising with superpixel segmentation and low-rank representation,” Information Sciences, vol. 397, pp. 48–68, 2017. (SCI, 中科院一区, 2019 IF=5.91)
  22. H. Guo, Y. Ma, X. Mei*, and J. Ma, “Infrared and visible image fusion based on total variation and augmented Lagrangian,” JOSA A, vol. 34, pp. 1961–1968, 2017. (SCI, 中科院三区, 2019 IF=1.791, Editors’ pick)
  23. Y. Ma, J. Wang, H. Xu, S. Zhang, X. Mei, and J. Ma, “Robust image feature matching via progressive sparse spatial consensus,” IEEE Access, vol. 5, pp. 24568–24579, 2017. (SCI, 中科院二区, 2019 IF=3.745)
  24. C. Li, Y. Ma, X. Mei*, F. Fan, J. Huang, and J. Ma, “Sparse unmixing of hyperspectral data with noise level estimation,” Remote Sensing, vol. 9, p. 1166, 2017. (SCI, 中科院二区, 2019 IF=4.509) [code]
  25. Y. Ma, C. Li, X. Mei*, C. Liu, and J. Ma, “Robust Sparse Hyperspectral Unmixing With $$\backslash$ell\_ $\{$2, 1$\}$ $ Norm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, pp. 1227–1239, 2017. (SCI, 中科院二区, 2019 IF=5.855) [code]
  26. C. Li, Y. Ma, J. Huang, X. Mei, C. Liu, and J. Ma, “GBM-based unmixing of hyperspectral data using bound projected optimal gradient method,” IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 952–956, 2016. (SCI, 中科院二区, 2019 IF=3.833)
  27. C. Li, Y. Ma, X. Mei, C. Liu, and J. Ma, “Hyperspectral image classification with robust sparse representation,” IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 641–645, 2016. (SCI, 中科院二区, 2019 IF=3.833)
  28. J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 452–456, 2016. (SCI, 中科院二区, 2019 IF=3.833)
  29. C. Li, Y. Ma, X. Mei, C. Liu, and J. Ma, “Hyperspectral unmixing with robust collaborative sparse regression,” Remote Sensing, vol. 8, p. 588, 2016. (SCI, 中科院二区, 2019 IF=4.509)
  30. Y. M. X. M. J Huang, “A Hybrid Spatial-Spectral Denoising Method for Infrared Hyperspectral Images Using 2DPCA,” Infrared Physics \& Technology, 2016. (SCI, 中科院三区, 2019 IF=2.379)
  31. T. Tian, X. Mei, Y. Yu, C. Zhang, and X. Zhang, “Automatic visible and infrared face registration based on silhouette matching and robust transformation estimation,” Infrared Physics \& Technology, vol. 69, pp. 145–154, 2015. (SCI, 中科院三区, 2019 IF=2.379)
  32. X. Mei, Y. Ma, C. Li, F. Fan, J. Huang, and J. Ma, “A real-time infrared ultra-spectral signature classification method via spatial pyramid matching,” Sensors, vol. 15, pp. 15868–15887, 2015. (SCI, 中科院三区, 2019 IF=3.275)
  33. C. Li, Y. Ma, J. Huang, X. Mei, and J. Ma, “Hyperspectral image denoising using the robust low-rank tensor recovery,” JOSA A, vol. 32, pp. 1604–1612, 2015. (SCI, 中科院三区, 2019 IF=1.791)
  34. J. Huang, Y. Ma, F. Fan, X. Mei, and Z. Liu, “A scene-based nonuniformity correction algorithm based on fuzzy logic,” Optical Review, vol. 22, pp. 614–622, 2015. 32. (SCI, 中科院四区, 2019 IF=1.068)
  35. X. Mei, Y. Ma, F. Fan, C. Li, C. Liu, J. Huang, and J. Ma, “Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints,” International Journal of Remote Sensing, vol. 36, pp. 4724–4747, 2015. (SCI, 中科院三区, 2019 IF=2.976)
  36. B. Zhou, S. Wang, Y. Ma, X. Mei, B. Li, H. Li, and F. Fan, “An infrared image impulse noise suppression algorithm based on fuzzy logic,” Infrared Physics \& Technology, vol. 60, pp. 346–358, 2013. (SCI, 中科院三区, 2019 IF=2.379)

会议文章:

  1. Q. Jin, Y. Ma, X. Mei, H. Li, and J. Ma, “uTDN: An Unsupervised Two-Stream Dirichlet-Net for Hyperspectral Unmixing”, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Jun. 2021.
  2. G. Fan, Y. Ma, J. Huang, X. Mei, and J. Ma, “Robust graph autoencoder for hyperspectral anomaly detection”, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Jun. 2021.
  3. E. Pan, Y. Ma, X. Mei, F. Fan, and J. Ma, “Unsupervised stacked capsule autoencoder for hyperspectral image classification”, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Jun. 2021.
  4. L. Luo, Q. Wan, J. Chen, Y. Wang and X. Mei, “Drone Image Stitching Guided by Robust Elastic Warping and Locality Preserving Matching,” IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 9212-9215 (Oral)
  5. E. Pan, Y. Ma, X. Dai, F. Fan, J. Huang, X. Mei, and J. Ma, “GRU with Spatial Prior for Hyperspectral Image Classification,” IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 967-970 (Oral)
  6. Q. Jin, Y. Ma, X. Mei, X. Dai, H. Li, F. Fan, and J. Huang, “Gaussian Mixture Model for Hyperspectral Unmixing with Low-Rank Representation,” IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 294-297 (Oral)
  7. E. Pan, Y. Ma, X. Mei, X. Dai, F. Fan, X. Tian, and J. Ma, “Spectral-Spatial Classification of Hyperspectral Image based on a Joint Attention Network,” IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 413-416 (Oral)
  8. F Fan, Y. Ma, X Dai, and X. Mei, “An optimization model for infrared image enhancement method based on p-q norm constrained by saliency value”, Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152P (10 April 2018) (Oral)
  9. H. Xia, G. Zhang, M. Chen and X. Mei, “Two-Round Cooperation Based Spectrum Sensing in Cognitive Radio Networks,” 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu, 2010, pp. 1-5
联系方式

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