梅晓光 讲师

博士,武汉大学电子信息学院讲师。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年至2019年于武汉大学电子信息学院从事博士后研究工作,后被聘为讲师。目前主要从事高/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。主持了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国家级科研项目。发表SCI论文30余篇,获发明专利7项,2017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等重要国际期刊审稿人。(English Portal)(Google Scholar)

教育背景

2003.9-2007.6 华中科技大学 通信工程  本科
2008.9-2011.6 华中师范大学 通信与信息系统  硕士
2012.9-2016.3 华中科技大学 电路与系统  博士

工作经历

2010.10-2012.7 中船重工第七二二研究所  嵌入式软件工程师
2016.6 –2019.4 武汉大学 电子信息学院  博士后
2019.5-至今    武汉大学 电子信息学院 讲师

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. 军委科技委前沿创新项目:18-163-12-ZT-004-015-01, **感知技术研究,2019-2020,100万
  2. 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
  3. 军委科技委前沿创新项目:**目标探测与识别技术,2018-2019,40万
  4. 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,2018-2020,80万
  5. 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,2020-2022,25万
  6. 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,2017-2018,5万
  7. 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,2017/01-2018/12,15万
参与
  1. “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,2017-2019
  2. 军委科技委前沿创新计划:****目标多波段实时融合探测技术,2017-2019
  3. 军委科技委前沿创新计划:****尾迹多阵列红外探测,2017-2018
  4. “十三五”海军装备预研创新:****目标探测技术,2017-2018
  5. 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,2017-2018
  6. “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,2016-2018
  7. 装备预研教育部联合基金:可见光/红外图像融合的****系统,2016.11-2018.10
  8. “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****技术,2016-2018
  9. “十三五”装备预研共用技术:红外****模块技术,2016-2020
  10. 国防探索研究重大项目:红外图谱成像****系统研究(项目编号:713112),2011-2015
  11. 863 计划项目:在轨操控过程中的****技术研究(项目编号:2011AA7044030),2011-2015
  12. 863 计划项目:**内波**探测技术(项目编号:2011AA7014047),2011-2015
  13. 863 计划项目:天基低轨远程空间****方法研究(项目编号:2015AA7046401),2015-2016
  14. 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,2013-2016
  15. 教育部支撑装备预先研究项目:基于超光谱目标探测识别信号处理机研究(项目编号:625010211)
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
学术论文
  1. Chen, J., Li, X., Luo, L., Mei, X.*, & Ma, J. (2020). Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Information Sciences, 508, 64-78.
  2. Jin, Q., Ma, Y., Pan, E., Fan, F., Huang, J., Li, H., … & Mei, X*. (2019). Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity. Remote Sensing, 11(20), 2434.[code]
  3. Ma, Y., Zhang, Y., Mei, X.*, Dai, X., & Ma, J. (2019). Multifeature-Based Discriminative Label Consistent K-SVD for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  4. Sui, C., Li, C., Feng, J., & Mei, X. (2019). Unsupervised Manifold-Preserving and Weakly Redundant Band Selection Method for Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing.
  5. Pan, E., Ma, Y., Mei, X*., Dai, X., Fan, F., Tian, X., & Ma, J. (2019, July). Spectral-Spatial Classification of Hyperspectral Image based on a Joint Attention Network. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 413-416). IEEE.
  6. Jin, Q., Ma, Y., Mei, X*., Dai, X., Li, H., Fan, F., & Huang, J. (2019, July). Gaussian Mixture Model for Hyperspectral Unmixing with Low-Rank Representation. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 294-297). IEEE.
  7. Pan, E., Ma, Y., Dai, X., Fan, F., Huang, J., Mei, X., & Ma, J. (2019, July). GRU with Spatial Prior for Hyperspectral Image Classification. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 967-970). IEEE.
  8. Luo, L., Wan, Q., Chen, J., Wang, Y., & Mei, X. (2019, July). Drone Image Stitching Guided by Robust Elastic Warping and Locality Preserving Matching. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 9212-9215). IEEE.
  9. Jiayi Ma, Han Xu, Junjun Jiang, Xiaoguang Mei*, and Xiao-Ping Zhang,“DDcGAN: A Dual-discriminator Conditional Generative Adversarial Network for Multi-resolution Image Fusion”,IEEE TRANSACTIONS ON IMAGE PROCESSING,Accepted(CCF A类,影响因子:6.79)
  10. Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., and Ma, J,”Spectral-Spatial Attention Networks for Hyperspectral Image Classification”. Remote Sensing 11. 2019 (SCI, IF=3.244, 二区)[code]
  11. Ma, Y., Jin, Q., Mei, X*., Dai, X., Fan, F., Li, H., & Huang, J, “Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation”. Remote Sensing, 11(8), 911. 2019 (SCI, IF=3.244, 二区)[code]
  12. 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.[code]
  13. 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, 2018.
  14. 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, 2018.
  15. 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, 2018-01-01 2018.
  16. Y. Ma, C. Li, X. Mei*, C. Liu, and J. Ma, “Robust Sparse Hyperspectral Unmixing With l2,1 Norm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, pp. 1227-1239, 2017.[code]
  17. H. Guo, Y. Ma, X. Mei*, and J. Ma, “Infrared and visible image fusion based on total variation and augmented Lagrangian,” Journal of the Optical Society of America A-Optics Image Science and Vision, vol. 34, pp. 1961-1968, 2017.
  18. 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, 2017.[code]
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. C. Li, Y. Ma, X. Mei, C. Liu, and J. Ma, “Hyperspectral Unmixing with Robust Collaborative Sparse Regression,” Remote Sensing, vol. 8, 2016.
  24. J. Huang, Y. Ma, X. Mei, and F. Fan, “A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA,” Infrared Physics & Technology, vol. 79, pp. 68-73, 2016.
  25. 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.
  26. 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.
  27. 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.
  28. C. Li, Y. Ma, J. Huang, X. Mei, and J. Ma, “Hyperspectral image denoising using the robust low-rank tensor recovery,” Journal of the Optical Society of America A-Optics Image Science and Vision, vol. 32, pp. 1604-1612, 2015.
  29. 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.
  30. 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.
  31. 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.
联系方式

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