梅晓光 (Xiaoguang Mei)

梅晓光 (Xiaoguang Mei)

博士，武汉大学电子信息学院副教授，博士生导师 (Ph.D., Associate Professor, Electronic Information School, Wuhan University)

👨‍🎓 Biosketch

Xiaoguang Mei is an associate professor at the Electronic Information School of Wuhan University and a recipient of the Hubei Province “Outstanding Youth Fund”. He received his BS and PhD degrees from Huazhong University of Science and Technology in 2007 and 2016, respectively. During his doctoral studies, he won the “National Scholarship for Doctoral Students”. He then entered Wuhan University to engage in postdoctoral research. His research interests are hyperspectral imaging systems and image analysis, including computer vision. He has published more than 100 papers in international authoritative journals such as IEEE TIP/TNNLS/TGRS/TII, Pattern Recognition, and international top conferences such as CVPR, AAAI, and IROS, including 5 highly cited papers, and 1 of them has entered the list of “TOP100 High-value Papers Published by Chinese Institutional Scholars in Computer Artificial Intelligence Journals in 2020” in the Global Scholars Database. He has authorized 8 national invention patents. He has led projects projects such as the National Key R&D Program, the Hubei Province Outstanding Youth Project, and the National Natural Science Foundation. The research results won the second prize in natural sciences of Hubei Province (No. 1 Award Winner), the second prize in natural sciences of the Chinese Society of Automation, and the Hsue-shen Tsien Paper Award in 2023 of the international journal IEEE/CAA Journal of Automatica Sinica.

📧 Contact info

📝 Publications

2025

  1. Yang Pei, Yong Ma, Xiaoguang Mei*, Qihai Chen, Minghui Wu, and Jiayi Ma. “Deep Blind Super-Resolution for Hyperspectral Images.” Pattern Recognition 157 (January 2025): 110916. https://doi.org/10.1016/j.patcog.2024.110916.

2024

  1. Yucheng Sun, Yong Ma, Yuan Yao, Xiaoguang Mei*, Jun Huang, and Jiayi Ma. “Domain Adaptation Hyperspectral Image Fusion Based on Spatial-Spectral Domain Separation.” Geo-Spatial Information Science, September 3, 2024, 1–15. https://doi.org/10.1080/10095020.2024.2380476
  2. Yu, Yang, Erting Pan, Yong Ma, Xiaoguang Mei*, Qihai Chen, and Jiayi Ma. “UnmixDiff: Unmixing-Based Diffusion Model for Hyperspectral Image Synthesis.” IEEE Transactions on Geoscience and Remote Sensing 62 (2024): 1–18. https://doi.org/10.1109/tgrs.2024.3425517.
  3. Pan, Erting, Yang Yu, Xiaoguang Mei, Jun Huang, and Jiayi Ma. “From the Abundance Perspective: Multi-Modal Scene Fusion-Based Hyperspectral Image Synthesis.” Information Fusion 108 (2024): 102419.
  4. Yantao Chen, Yong Ma, Xiaoguang Mei, Lin Zhang, Zhigang Fu, and Jiayi Ma. “Triple-Task Mutual Consistency for Semi-Supervised 3D Medical Image Segmentation.” Computers in Biology and Medicine, 2024.
  5. Yingsong Cheng, Xinya Wang, Yong Ma, Xiaoguang Mei*, Minghui Wu, and Jiayi Ma. “General Hyperspectral Image Super-Resolution via Meta-Transfer Learning.” IEEE Transactions on Neural Networks and Learning Systems, 2024.
  6. Wendi Liu, Yong Ma, Xiaozhu Wang, Jun Huang, Qihai Chen, Hao Li, Xiaoguang Mei*, “UADNet: A Joint Unmixing and Anomaly Detection Network Based on Deep Clustering for Hyperspectral Image,” in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2024.33759341.
  7. Yifan Lu, Jiayi Ma, Xiaoguang Mei, Jun Huang, Xiao-Ping (Steven) Zhang, Feature Matching via Topology-Aware Graph Interaction Model. IEEE CAA J. Autom. Sinica 11(1): 113-130 (2024)
  8. Yang Yu, Erting Pan, Xinya Wang, Yuheng Wu, Xiaoguang Mei*, Jiayi Ma, Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-based Hyperspectral Image Synthesis, The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024, Seattle, USA, 2024
  9. Hebaixu Wang, Meiqi Gong, Xiaoguang Mei, Hao Zhang, and Jiayi Ma. “Deep Unfolded Network with Intrinsic Supervision for Pan-Sharpening”, Proceedings of the AAAI Conference on Artificial Intelligence 38.6 (AAAI 2024): 5419-5426.
  10. Cai, Zhao, Yong Ma, Jun Huang, Xiaoguang Mei, Fan Fan, and Zhiqing Zhao. “CMFuse: Cross-Modal Features Mixing via Convolution and MLP for Infrared and Visible Image Fusion.” IEEE Sensors Journal 24, no. 15 (August 1, 2024): 24152–67. https://doi.org/10.1109/jsen.2024.3410387.
  11. 柴静雯, 李安康, 张浩, 马泳, 梅晓光, & 马佳义. (2024). 结合局部全局特征与多尺度交互的三维多器官分割网络. 中国图象图形学报, 29(3), 655-669.
  12. Du, You, Yong Ma, Jun Huang,Xiaoguang Mei, Jinhui Qin, and Fan Fan. “IRLF-SRNet: A Super-Resolution Network Based on Local–Global Feature Enhance-Refine for Camera-Array Based Infrared Light Field Images.” Infrared Physics & Technology 141 (August 10, 2024): 105494–94. https://doi.org/10.1016/j.infrared.2024.105494.

2023

  1. Pan, E., Ma, Y., Mei, X., Huang, J., Chen, Q., & Ma, J. (2023). Hyperspectral image destriping and denoising from a task decomposition view. Pattern Recognition, 109832.
  2. Liu, W., Mei, X., Ma, Y., Huang, J., Chen, Q., & Li, H. (2023). Graph L1-Laplacians Regularized GMM for Hyperspectral Unmixing. IEEE Geoscience and Remote Sensing Letters.
  3. Pan, E., Ma, Y., Mei, X.*, Fan, F., Huang, J., & Ma, J. (2023). Progressive Hyperspectral Image Destriping with an Adaptive Frequencial Focus. IEEE Transactions on Geoscience and Remote Sensing.
  4. Chu, M., Ma, Y., Mei, X., Huang, J., & Fan, F. (2023). Learning-based correspondence classifier with self-attention hierarchical network. Applied Intelligence, 1-17.
  5. W. Dong, Y. Chen, A. Li, X. Mei & Y. Yang, “Automatic detection of adenoid hypertrophy on cone-beam computed tomography based on deep learning,” American Journal of Orthodontics and Dentofacial Orthopedics, 163(4), 553-560, 2023 (On the cover)
  6. Pan, E., Ma, Y., Mei, X., Fan, F., & Ma, J. (2023). Hyperspectral image denoising via spectral noise distribution bootstrap. Pattern Recognition, 142, 109699.
  7. Li, Z., Ma, Y., Mei, X., & Ma, J. (2023). Two-view correspondence learning using graph neural network with reciprocal neighbor attention. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 114-124.
  8. Peng, Z., Ma, Y., Zhang, Y., Li, H., Fan, F., & Mei, X. (2023). Seamless UAV hyperspectral image stitching using optimal seamline detection via graph cuts. IEEE Transactions on Geoscience and Remote Sensing.
  9. Cai, Z., Ma, Y., Huang, J., Mei, X., & Fan, F. (2023). Correlation-Guided Discriminative Cross-Modality Features Network for Infrared and Visible Image Fusion. IEEE Transactions on Instrumentation and Measurement.
  10. Sun, Y., Xu, H., Ma, Y., Wu, M., Mei, X., Huang, J., & Ma, J. (2023). Dual Spatial-spectral Pyramid Network with Transformer for Hyperspectral Image Fusion. IEEE Transactions on Geoscience and Remote Sensing.
  11. 靳淇文, 马泳, 樊凡, 黄珺, 李皞, & 梅晓光. (2023). 生成对抗网络的无监督高光谱解混. Journal of Remote Sensing, 27(8).

2022

  1. A. Fan, J. Ma, X. Tian, X. Mei, W. Liu,”Coherent Point Drift Revisited for Non-Rigid Shape Matching and Registration,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  2. 徐涵, 梅晓光*, 樊凡, 马泳, & 马佳义, 信息分离和质量引导的红外与可见光图像融合, 中国图象图形学报, 27(11), 3316-3330, 2022
  3. X. Wang, Y. Cheng, X. Mei, J. Jiang and J. Ma, “Group Shuffle and Spectral-Spatial Fusion for Hyperspectral Image Super-Resolution,” in IEEE Transactions on Computational Imaging, vol. 8, pp. 1223-1236, 2022
  4. E. Pan, Y. Ma, X. Mei*, J. Huang, F. Fan and J. Ma, “Letter: D2Net: Deep denoising network in frequency domain for hyperspectral image,” in IEEE/CAA Journal of Automatica Sinica
  5. H. Xu, Y. Sun, X. Mei, X. Tian and J. Ma, “Attention-Guided Polarization Image Fusion Using Salient Information Distribution,” in IEEE Transactions on Computational Imaging, vol. 8, pp. 1117-1130, 2022
  6. Y. Yao, M. Wang, G. Fan, W. Liu, Y. Ma, X. Mei, “Dictionary Learning-Cooperated Matrix Decomposition for Hyperspectral Target Detection. Remote Sensing,” 14(17), 4369, 2022
  7. Y. Yu, Y. Ma, X. Mei*, F. Fan, J. Huang, H. Li, “Multi-stage convolutional autoencoder network for hyperspectral unmixing. International Journal of Applied Earth Observation and Geoinformation,” 113, 102981, 2022
  8. S. Mei, Y. Ma, X. Mei, J. Huang, F. Fan, “S2-Net: Self-Supervision Guided Feature Representation Learning for Cross-Modality Images. IEEE/CAA Journal of Automatica Sinica,” 9(10), 1883-1885, 2022
  9. Y. Zhang, X. Mei, Y. Ma, X. Jiang, Z. Peng, J. Huang, “Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment,” Remote Sensing, 4(16): 4038, 2022
  10. Z. Le, J. Huang, H. Xu, F. Fan, Y. Ma, X. Mei, J. Ma, “UIFGAN: An unsupervised continual-learning generative adversarial network for unified image fusion,” Information Fusion, 2022
  11. Z. Li, Y. Ma, X. Mei, J. Huang and J. Ma, “Guided neighborhood affine subspace embedding for feature matching,” Pattern Recogn., vol. 124, pp. 108489, 2022
  12. Q. Jin, Y. Ma, X. Mei* and J. Ma, “TANet: An Unsupervised Two-Stream Autoencoder Network for Hyperspectral Unmixing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.
  13. E. Pan, Y. Ma, X. Mei*, F. Fan, J. Huang and J. Ma, “SQAD: Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising,” IEEE T. Geosci. Remote, vol. 60, pp. 1-14, 2022
  14. J. Ma, L. Tang, F. Fan, J. Huang, X. Mei, Y. Ma, “SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer,” IEEE/CAA Journal of Automatica Sinica, 9(7), 1200-1217, 2022. Hsue-shen Tsien Paper Award（钱学森论文奖，IEEE/CAA JAS唯一最佳论文奖） 🔥🔥
  15. Zhao-bing Qiu, Yong Ma, Fan Fan, Jun Huang, Ming-hui Wu, Xiao-guang Mei, A pixel-level local contrast measure for infrared small target detection, Defence Technology, Volume 18, Issue 9, 2022, Pages 1589-1601,
  16. 樊港辉, 马泳, 梅晓光*, 黄珺, 樊凡, & 李皞. 空域协同自编码器的高光谱异常检测. 中国图象图形学报, 27(10): 3116-3126, 2022
  17. C. Ma, J. Jiang, H. Li, X. Mei & C. Bai, “Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer. Remote Sensing,” 14(19), 4732, 2022

2021

  1. 徐涵, 梅晓光*, 樊凡, 马泳 and 马佳义, “分离表征和指标驱动的红外可见光图像融合,” 中国图象图形学报, 2021.
  2. Y. Zhang, Y. Ma, X. Dai, H. Li, X. Mei* and J. Ma, “Locality-constrained sparse representation for hyperspectral image classification,” Inform. Sciences, vol. 546, pp. 858-870, 2021.
  3. C. Sui, J. Zhou, C. Li, Q. Zhang, J. Feng, X. Mei, J. Wang, “Unsupervised hyperspectral band selection with multigraph integrated embedding and robust self-contained regression,” IEEE T. Geosci. Remote, vol. 60, pp. 1-15, 2021.
  4. E. Pan, Y. Ma, F. Fan, X. Mei and J. Huang, “Hyperspectral Image Classification across Different Datasets: A Generalization to Unseen Categories,” Remote Sens.-Basel, vol. 13, no. 9, pp. 1672, 2021.
  5. J. Ma, X. Ye, H. Zhou, X. Mei and F. Fan, “Loop-closure detection using local relative orientation matching,” IEEE T. Intell. Transp., 2021.
  6. Z. Qiu, Y. Ma, F. Fan, J. Huang, M. Wu and X. Mei, “A pixel-level local contrast measure for infrared small target detection,” Defence Technology, 2021.
  7. G. Fan, Y. Ma, X. Mei*, F. Fan, J. Huang and J. Ma, “Hyperspectral anomaly detection with robust graph autoencoders,” IEEE T. Geosci. Remote, 2021. (ESI Highly Cited Paper) 🔥🔥
  8. H. Li, Y. Zhang, Y. Ma, X. Mei, S. Zeng and Y. Li, “Pairwise Elastic Net Representation-Based Classification for Hyperspectral Image Classification,” Entropy-Switz., vol. 23, no. 8, pp. 956, 2021.
  9. Z. Peng, Y. Ma, X. Mei*, J. Huang and F. Fan, “Hyperspectral Image Stitching via Optimal Seamline Detection,” IEEE Geosci. Remote S., vol. 19, pp. 1-5, 2021.
  10. Q. Jin, Y. Ma, F. Fan, J. Huang, X. Mei* and J. Ma, “Adversarial Autoencoder Network for Hyperspectral Unmixing,” IEEE T. Neur. Net. Lear., 2021.
  11. J. Ma, S. Wang, K. Zhang, Z. He, J. Huang and X. Mei, “Fast and Robust Loop-closure Detection via Convolutional Auto-encoder and Motion Consensus,” IEEE T. Ind. Inform., 2021.
  12. A. Fan, X. Jiang, Y. Ma, X. Mei and J. Ma, “Smoothness-Driven Consensus Based on Compact Representation for Robust Feature Matching,” IEEE T. Neur. Net. Lear., 2021.
  13. Y. Yu, Y. Ma, X. Mei, F. Fan, J. Huang and J. Ma, “A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching,” Remote Sens.-Basel, vol. 13, no. 23, pp. 4912, 2021.
  14. G. Fan, Y. Ma, J. Huang, X. Mei, and J. Ma, “Robust graph autoencoder for hyperspectral anomaly detection,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 1830–1834.
  15. Q. Jin, Y. Ma, X. Mei, H. Li, and J. Ma, “Utdn: An unsupervised two-stream dirichlet-net for hyperspectral unmixing,” in ICASSP 2021- 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 1885–1889.
  16. E. Pan, Y. Ma, X. Mei, F. Fan, and J. Ma, “Unsupervised stacked capsule autoencoder for hyperspectral image classification,” in ICASSP 2021- 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 1825–1829.
  17. K. Zhang, X. Jiang, X. Mei, H. Zhou, and J. Ma, “Motion field consensus with locality preservation: A geometric confirmation strategy for loop closure detection,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 445–451.

2020

  1. J. Chen, X. Li, L. Luo, X. Mei* and J. Ma, “Infrared and visible image fusion based on target-enhanced multiscale transform decomposition,” Inform. Sciences, vol. 508, pp. 64-78, 2020. (ESI Highly Cited Paper) 🔥🔥
  2. 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.
  3. 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 T. Image Process., vol. 29, pp. 4980-4995, 2020. (ESI Highly Cited Paper) 🔥🔥
  4. 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 and Applications, vol. 32, no. 18, pp. 15119-15129, 2020.
  5. Y. Wang, X. Mei, Y. Ma, J. Huang, F. Fan and J. Ma, “Learning to find reliable correspondences with local neighborhood consensus,” Neurocomputing, vol. 406, pp. 150-158, 2020.
  6. 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, no. 7, pp. 1105-1115, 2020.
  7. Y. Zhang, Z. Wan, X. Jiang and X. Mei*, “Automatic stitching for hyperspectral images using robust feature matching and elastic warp,” Ieee J.-Stars, vol. 13, pp. 3145-3154, 2020.
  8. 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 Geosci. Remote S., vol. 18, no. 8, pp. 1436-1440, 2020.

2019

  1. J. Wang, J. Chen, H. Xu, S. Zhang, X. Mei*, J. Huang, J. Ma, “Gaussian field estimator with manifold regularization for retinal image registration,” Signal Process., vol. 157, pp. 225-235, 2019.
  2. Y. Ma, Y. Wang, X. Mei, C. Liu, X. Dai, F. Fan, 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.
  3. Y. Ma, Q. Jin, X. Mei*, X. Dai, F. Fan, H. Li, J. Huang, “Hyperspectral unmixing with Gaussian mixture model and low-rank representation,” Remote Sens.-Basel, vol. 11, no. 8, pp. 911, 2019.
  4. X. Mei, E. Pan, Y. Ma, X. Dai, J. Huang, F. Fan, Q. Du, H. Zheng, J. Ma, “Spectral-spatial attention networks for hyperspectral image classification,” Remote Sens.-Basel, vol. 11, no. 8, pp. 963, 2019. (ESI Highly Cited Paper) 🔥🔥
  5. Q. Jin, Y. Ma, E. Pan, F. Fan, J. Huang, H. Li, C. Sui, X. Mei*, “Hyperspectral unmixing with Gaussian mixture model and spatial group sparsity,” Remote Sens.-Basel, vol. 11, no. 20, pp. 2434, 2019.
  6. C. Sui, C. Li, J. Feng and X. Mei, “Unsupervised manifold-preserving and weakly redundant band selection method for hyperspectral imagery,” IEEE T. Geosci. Remote, vol. 58, no. 2, pp. 1156-1170, 2019.
  7. Y. Ma, Y. Zhang, X. Mei*, X. Dai and J. Ma, “Multifeature-based discriminative label consistent k-svd for hyperspectral image classification,” Ieee J.-Stars, vol. 12, no. 12, pp. 4995-5008, 2019.
  8. 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.
  9. L. Luo, Q. Wan, J. Chen, Y. Wang, and X. Mei, “Drone image stitching guided by robust elastic warping and locality preserving matching,” in IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 9212–9215.
  10. E. Pan, Y. Ma, X. Dai, F. Fan, J. Huang, X. Mei, and J. Ma, “GRU with spatial prior for hyperspectral image classification,” in IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 967–970.
  11. 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,” in IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 294–297.
  12. 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,” in IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 413–416.

2018

  1. 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.
  2. Y. Ma, C. Li, H. Li, X. Mei and J. Ma, “Hyperspectral image classification with discriminative kernel collaborative representation and Tikhonov regularization,” IEEE Geosci. Remote S., vol. 15, no. 4, pp. 587-591, 2018.
  3. 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.

2017

  1. F. Fan, Y. Ma, C. Li, X. Mei, J. Huang and J. Ma, “Hyperspectral image denoising with superpixel segmentation and low-rank representation,” Inform. Sciences, vol. 397, pp. 48-68, 2017.
  2. 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, no. 11, pp. 1961-1968, 2017. (Editors' Pick) 🔥🔥
  3. 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.
  4. C. Li, Y. Ma, X. Mei*, F. Fan, J. Huang and J. Ma, “Sparse unmixing of hyperspectral data with noise level estimation,” Remote Sens.-Basel, vol. 9, no. 11, pp. 1166, 2017. (code)
  5. Y. Ma, C. Li, X. Mei*, C. Liu and J. Ma, “Robust Sparse Hyperspectral Unmixing With $\ell_ {2, 1} $ Norm,” IEEE T. Geosci. Remote, vol. 55, no. 3, pp. 1227-1239, 2017. (code)

2016

  1. 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 Geosci. Remote S., vol. 13, no. 7, pp. 952-956, 2016.
  2. C. Li, Y. Ma, X. Mei, C. Liu and J. Ma, “Hyperspectral image classification with robust sparse representation,” IEEE Geosci. Remote S., vol. 13, no. 5, pp. 641-645, 2016.
  3. J. Han, Y. Ma, J. Huang, X. Mei and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote S., vol. 13, no. 3, pp. 452-456, 2016.
  4. C. Li, Y. Ma, X. Mei, C. Liu and J. Ma, “Hyperspectral unmixing with robust collaborative sparse regression,” Remote Sens.-Basel, vol. 8, no. 7, pp. 588, 2016.
  5. J. Huang, Y. Ma, X. Mei, F. Fan, “A Hybrid Spatial-Spectral Denoising Method for Infrared Hyperspectral Images Using 2DPCA,” Infrared Phys. Techn., 2016.

Before 2015

  1. 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 Phys. Techn., vol. 69, pp. 145-154, 2015.
  2. 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-Basel, vol. 15, no. 7, pp. 15868-15887, 2015.
  3. 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, no. 9, pp. 1604-1612, 2015.
  4. J. Huang, Y. Ma, F. Fan, X. Mei and Z. Liu, “A scene-based nonuniformity correction algorithm based on fuzzy logic,” Opt. Rev., vol. 22, no. 4, pp. 614-622, 2015.
  5. X. Mei, Y. Ma, F. Fan, C. Li, C. Liu, J. Huang, J. Ma, “Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints,” Int. J. Remote Sens., vol. 36, no. 18, pp. 4724-4747, 2015.
  6. B. Zhou, S. Wang, Y. Ma, X. Mei, B. Li, H. Li, F. Fan, “An infrared image impulse noise suppression algorithm based on fuzzy logic,” Infrared Phys. Techn., vol. 60, pp. 346-358, 2013.