樊凡副教授

樊凡，1989，博士，武汉大学副教授。2009 年获华中科技大学通信工程学士学位，2015 年获于华中科技大学电子与信息工程系电子科学与技术博士学位，博士期间获得研究生国家奖学金，毕业后受聘于武汉大学电子信息学院信息与通信工程系，现为武汉大学副教授。作为课题负责人承担了“十三五”装备预研专用技术、“十三五”装备预研领域基金、国家自然科学基、湖北省自然科学基金、中国博士后科学基金、中央高校自主科研项目、中电科横向课题等项目研究工作，是湖北省创新群体“时频域图像信息获取与智能识别”的骨干成员。在项目的支持下，具体开展了红外成像预处理、高光谱图像预处理、红外与可见光图像融合等方面研究，为提高小目标检测率与识别率奠定了物质基础。研究成果撰写为学术论文在 Information Sciences 、JAS、Nerocomputing、Infrared Physics & Technology、JOSA A 等SCI 国际期刊论文50余篇，其中一/通作19 篇。2018 年获得湖北省自然科学一等奖“高效高精准图像匹配理论及其应用研究”(排名第3)。2017 年获得中国自动化学会自然科学奖二等奖“视觉感知的图像高精准匹配的若干基础问题研究” (排名第5)。

教育背景

2005.9-2009.6 华中科技大学电子与信息工程系提高班本科
2009.9-2015.6 华中科技大学电子与信息工程系博士

工作经历

2015.10-2018.10，武汉大学电子信息学院，3+3博士后
2018.10-2019.6，武汉大学遥感信息工程学院，讲师
2019.10-2021.4，武汉大学电子信息学院，讲师
2021.4-至今，武汉大学电子信息学院，副教授

科研奖励

[1] 湖北省自然科学一等奖，高效高精准图像匹配理论及其应用研究，2018
[2] 中国自动化学会自然科学将二等奖，视觉感知的图像高精准匹配的若干基础问题研究，2017

获得专利

[1] 一种基于空间分层匹配的超光谱分类方法和装置，申请号：CN 201510482142.0
[2] 一种基于梯度传递的可见光与红外图像融合方法，申请号:CN 201510416252.7
[3] 基于边缘提取的红外图像的直方图增强方法，申请号：CN 201410834788.6
[4] 扫描型红外成像系统的场景非均匀校正方法，专利号：ZL201110345226.1
[5] 扫描型红外成像系统的非均匀校正方法，专利号：ZL201110312154.0
[6] 一种通过长短积分控制实现红外图像增强的方法，专利号：ZL201010502552.4

学术论文

[1] Le Z, Huang J, Xu H, et al. UIFGAN: An unsupervised continual-learning generative adversarial network for unified image fusion[J]. Information Fusion, 2022, 88: 305-318.

[2] Pan E, Ma Y, Mei X, et al. D2Net: Deep denoising network in frequency domain for hyperspectral image[J]. IEEE/CAA Journal of Automatica Sinica, 2022.

[3] Mei S, Ma Y, Mei X, et al. S2-Net: Self-Supervision Guided Feature Representation Learning for Cross-Modality Images[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(10): 1883-1885.

[4] Yu Y, Ma Y, Mei X, et al. Multi-stage convolutional autoencoder network for hyperspectral unmixing[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 113: 102981.

[5] Qiu Z, Ma Y, Fan F, et al. A pixel-level local contrast measure for infrared small target detection[J]. Defence Technology, 2022, 18(9): 1589-1601.

[6] Ma Y, Wang X, Gao W, et al. Progressive Fusion Network Based on Infrared Light Field Equipment for Infrared Image Enhancement[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(9): 1687-1690.

[7] Qiu Z, Ma Y, Fan F, et al. Global Sparsity-Weighted Local Contrast Measure for Infrared Small Target Detection[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.

[8] Ma J, Tang L, Fan F, et al. Swinfusion: cross-domain long-range learning for general image fusion via swin transformer[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(7): 1200-1217.

[9] Wu L, Fang S, Ma Y, et al. Infrared small target detection based on gray intensity descent and local gradient watershed[J]. Infrared Physics & Technology, 2022, 123: 104171.

[10] Pan E, Ma Y, Mei X, et al. SQAD: Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.

[11] Ma J, Gao W, Ma Y, et al. Learning Spatial-Parallax Prior Based on Array Thermal Camera for Infrared Image Enhancement[J]. IEEE Transactions on Industrial Informatics, 2021.

[12] Jin Q, Ma Y, Fan F, et al. Adversarial autoencoder network for hyperspectral unmixing[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021.

[13] Peng Z, Ma Y, Mei X, et al. Hyperspectral Image Stitching via Optimal Seamline Detection[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.

[14] Fan G, Ma Y, Mei X, et al. Hyperspectral anomaly detection with robust graph autoencoders[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-14.

[15] Pan E, Ma Y, Mei X, et al. Unsupervised stacked capsule autoencoder for hyperspectral image classification[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 1825-1829.

[16] Ma J, Ye X, Zhou H, et al. Loop-closure detection using local relative orientation matching[J]. IEEE Transactions on Intelligent Transportation Systems, 2021.

[17] Yu Y, Ma Y, Mei X, et al. A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching[J]. Remote Sensing, 2021, 13(23): 4912.

[18] Pan E, Ma Y, Fan F, et al. Hyperspectral image classification across different datasets: A generalization to unseen categories[J]. Remote Sensing, 2021, 13(9): 1672.

[19] Qiu Z, Ma Y, Fan F, et al. Adaptive scale patch-based contrast measure for dim and small infrared target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2020.

[20] Wang Y, Mei X, Ma Y, et al. Learning to find reliable correspondences with local neighborhood consensus[J]. Neurocomputing, 2020, 406: 150-158.

[21] Huang J, Le Z, Ma Y, et al. A generative adversarial network with adaptive constraints for multi-focus image fusion[J]. Neural Computing and Applications, 2020, 32(18): 15119-15129.

[22] Wu L, Ma Y, Fan F, et al. A double-neighborhood gradient method for infrared small target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 18(8): 1476-1480.

[23] Huang J, Le Z, Ma Y, et al. Mgmdcgan: Medical image fusion using multi-generator multi-discriminator conditional generative adversarial network[J]. IEEE Access, 2020, 8: 55145-55157.

[24] Xu H, Fan F, Zhang H, et al. A deep model for multi-focus image fusion based on gradients and connected regions[J]. IEEE Access, 2020, 8: 26316-26327.

[25] Xiao S, Ma Y, Fan F, et al. Tracking small targets in infrared image sequences under complex environmental conditions[J]. Infrared Physics & Technology, 2020, 104: 103102.

[26] Yang Z, Chen Y, Le Z, et al. Multi-source medical image fusion based on Wasserstein generative adversarial networks[J]. IEEE Access, 2019, 7: 175947-175958.

[27] Jin Q, Ma Y, Mei X, et al. Gaussian mixture model for hyperspectral unmixing with low-rank representation[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019: 294-297.

[28] Pan E, Ma Y, Dai X, et al. GRU with spatial prior for hyperspectral image classification[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019: 967-970.

[29] Pan E, Ma Y, Mei X, et al. Spectral-spatial classification of hyperspectral image based on a joint attention network[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019: 413-416.

[30] Jin Q, Ma Y, Pan E, et al. Hyperspectral unmixing with Gaussian mixture model and spatial group sparsity[J]. Remote Sensing, 2019, 11(20): 2434.

[31] Zheng Z, Zheng H, Ma Y, et al. Feedback unilateral grid-based clustering feature matching for remote sensing image registration[J]. Remote Sensing, 2019, 11(12): 1418.

[32] Mei X, Pan E, Ma Y, et al. Spectral-spatial attention networks for hyperspectral image classification[J]. Remote Sensing, 2019, 11(8): 963.

[33] Ma Y, Jin Q, Mei X, et al. Hyperspectral unmixing with Gaussian mixture model and low-rank representation[J]. Remote Sensing, 2019, 11(8): 911.

[34] Ma Y, Wang Y, Mei X, et al. Visible/infrared combined 3D reconstruction scheme based on nonrigid registration of multi-modality images with mixed features[J]. IEEE Access, 2019, 7: 19199-19211.

[35] Du Q, Xu H, Ma Y, et al. Fusing infrared and visible images of different resolutions via total variation model[J]. Sensors, 2018, 18(11): 3827.

[36] Fan F, Ma Y, Huang J, et al. Infrared image enhancement based on saliency weight with adaptive threshold[C]//2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP). IEEE, 2018: 225-230.

[37] Fan F, Ma Y, Dai X, et al. An optimization model for infrared image enhancement method based on pq norm constrained by saliency value[C]//Ninth International Conference on Graphic and Image Processing (ICGIP 2017). SPIE, 2018, 10615: 769-774.

[38] Mei X, Ma Y, Li C, et al. Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation[J]. Neurocomputing, 2018, 275: 2783-2797.

[39] Du Q, Fan A, Ma Y, et al. Infrared and visible image registration based on scale-invariant piifd feature and locality preserving matching[J]. IEEE Access, 2018, 6: 64107-64121.

[40] Liu Z, Ma Y, Fan F, et al. Nonuniformity correction based on adaptive sparse representation using joint local and global constraints based learning rate[J]. IEEE Access, 2018, 6: 10822-10839.

[41] Huang J, Ma Y, Zhang Y, et al. Infrared image enhancement algorithm based on adaptive histogram segmentation[J]. Applied optics, 2017, 56(35): 9686-9697.

[42] Li C, Ma Y, Mei X, et al. Sparse unmixing of hyperspectral data with noise level estimation[J]. Remote Sensing, 2017, 9(11): 1166.

[43] Fan F, Ma Y, Li C, et al. Hyperspectral image denoising with superpixel segmentation and low-rank representation[J]. Information Sciences, 2017, 397: 48-68.

[44] Zhang X, Ma Y, Fan F, et al. Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition[J]. JOSA A, 2017, 34(8): 1400-1410.

[45] Huang J, Ma Y, Mei X, et al. A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA[J]. Infrared Physics & Technology, 2016, 79: 68-73.

[46] Ma Y, Chen J, Chen C, et al. Infrared and visible image fusion using total variation model[J]. Neurocomputing, 2016, 202: 12-19.

[47] Liu Z, Ma Y, Huang J, et al. A registration based nonuniformity correction algorithm for infrared line scanner[J]. Infrared Physics & Technology, 2016, 76: 667-675.

[48] Mei X, Ma Y, Fan F, et al. Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints[J]. International Journal of Remote Sensing, 2015, 36(18): 4724-4747.

[49] Huang J, Ma Y, Fan F, et al. A scene-based nonuniformity correction algorithm based on fuzzy logic[J]. Optical Review, 2015, 22(4): 614-622.

[50] Mei X, Ma Y, Li C, et al. A real-time infrared ultra-spectral signature classification method via spatial pyramid matching[J]. Sensors, 2015, 15(7): 15868-15887.

[51] Fan F, Ma Y, Huang J, et al. A combined temporal and spatial deghosting technique in scene based nonuniformity correction[J]. Infrared Physics & Technology, 2015, 71: 408-415.

[52] Han J, Ma Y, Zhou B, et al. A robust infrared small target detection algorithm based on human visual system[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2168-2172.

[53] Fan F, Ma Y, Zhou B, et al. A scene based nonuniformity correction algorithm for line scanning infrared image[J]. Optical Review, 2014, 21(6): 778-786.

[54] Zhou B, Wang S, Ma Y, et al. An infrared image impulse noise suppression algorithm based on fuzzy logic[J]. Infrared Physics & Technology, 2013, 60: 346-358.