教育管理

李畅

编辑日期2017-05-04    作者:    阅读次数:1856

姓    名李畅
职    称讲师                                
所属系生物医学工程系
邮    箱changli@hfut.edu.cn
电    话
  • 个人简历

    2018年3月获得华中科技大学工学博士学位,同年4月进入合肥工业大学仪器科学与光电工程学院任职。已在IEEE TGRS、IEEE GRSL、Information Fusion等国内外知名期刊会议上发表学术论文20余篇,其中SCI期刊论文20篇,论文总被引400余次。


  • 研究领域

    生医信号处理、医学图像分析、遥感图像处理、机器学习、计算机视觉、信息融合

  • 开设课程

    信号与系统

    医学图像处理
  • 科研项目

  • 发表论文

    期刊论文(*通信作者)

    [1] Jiayi Ma, Yong Ma, Chang Li, “Infrared and visible image fusion methods and applications: A survey”, Information Fusion, Vol. 45, pp. 153-178, 2019.

    [2] Jiayi Ma, Wei Yu, Pengwei Liang, Chang Li, Junjun Jiang, “FusionGAN: A generative adversarial network for infrared and visible image fusion”, Information Fusion, Vol. 48, pp. 11-26, 2019.

    [3] Jing Li, Hongtao Huo, Chenhong Sui, Chenchen Jiang, Chang Li, “Poisson reconstruction-based fusion of infrared and visible images via saliency detection”, IEEE Access, Vol. 7, No. 1, 2019.

    [4] Xiaoguang Mei, Yong Ma, Chang Li*, Fan Fan, Jun Huang, Jiayi Ma, “Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation”, Neurocomputing, Vol. 275, pp. 2783-2797, 2018.

    [5] Yong Ma, Chang Li*, Hao Li, Xiaoguang Mei, Jiayi Ma, “Hyperspectral Image Classification with Discriminative Kernel Collaborative Representation and Tikhonov Regularization”, IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 4, pp. 587-591, 2018.

    [6] Tian Tian, Chang Li, Jinkang Xu, Jiayi Ma, “Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks”, Sensors, Vol. 18, No. 3, pp. 904, 2018.

    [7] Haiyan Fan, Chang Li, Yulan Guo, Gangyao Kuang, Jiayi Ma, “Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 10, pp. 6196-6213, 2018.

    [8] Chang Li, Yu Liu, Juan Cheng, Rencheng Song, Hu Peng, Qiang Chen, Xun Chen, “Hyperspectral Unmixing with Bandwise Generalized Bilinear Model”, Remote Sensing, Vol. 10, No. 10, pp. 1600, 2018.

    [9] Juan Cheng, Fulin Wei, Chang Li, Yu Liu, Aiping Liu, Xun Chen,“Position-independent gesture recognition using sEMG signals via canonical correlation analysis”, Computers in Biology and Medicine, Vol. 103, pp. 44-54, 2018.

    [10] Fan Fan, Yong Ma, Chang Li*, Xiaoguang Mei, Jun Huang, Jiayi Ma, “Hyperspectral image denoising with superpixel segmentation and low-rank representation”, Information Sciences, Vol. 397, pp. 4868, 2017.

    [11] Hao Li, Chang Li*, Cong Zhang, Zhe Liu, Chengyin Liu, “Hyperspectral Image Classification with Spatial Filtering and l2,1 Norm”, Sensors, Vol. 17, No. 2, pp. 314, 2017.

    [12] Yong Ma, Chang Li, Xiaoguang Mei, Chengyin Liu, Jiayi Ma, “Robust Sparse Hyperspectral Unmixing With l2,1 Norm”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 3, pp. 1227-1239, 2017.

    [13] Chang Li, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, Jiayi Ma, “Sparse Unmixing of Hyperspectral Data with Noise Level Estimation”, Remote Sensing, Vol. 9, No. 11, pp. 1166, 2017.

    [14] Jiayi Ma, Chen Chen, Chang Li, Jun Huang, “Infrared and visible image fusion via gradient transfer and total variation minimization”, Information Fusion, Vol. 31, pp. 100-109, 2016. (ESI Highly Cited Paper)

    [15] Chang Li, Yong Ma, Xiaoguang Mei, Chengyin Liu, Jiayi Ma, “Hyperspectral Image Classification with Robust Sparse Representation”, IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 5, pp. 641-645, 2016.

    [16] Chang Li, Yong Ma, Jun Huang, Xiaoguang Mei, Chengyin Liu, Jiayi Ma, “GBM-Based Unmixing of Hyperspectral Data Using Bound Projected Optimal Gradient Method”, IEEE

    Geoscience and Remote Sensing Letters, Vol. 13, No. 7, pp. 952-956, 2016.

    [17] Chang Li, Yong Ma, Xiaoguang Mei, Chengyin Liu, Jiayi Ma, “Hyperspectral Unmixing with Robust Collaborative Sparse Regression”, Remote Sensing, Vol. 8, No. 7, pp. 588, 2016.

    [18] Chang Li, Yong Ma, Jun Huang, Xiaoguang Mei, Jiayi Ma, “Hyperspectral image denoising using the robust low-rank tensor recovery”, Journal of the Optical Society of America A, Vol. 32, No. 9, pp. 1604-1612, 2015.

    [19] Xiaoguang Mei, Yong Ma, Chang Li, Fan Fan, Jun Huang, Jiayi Ma, “A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching”, Sensors, Vol. 15, No. 7, pp. 15868-15887, 2015.

    [20] Xiaoguang Mei, Yong Ma, Fan Fan, Chang Li, Chengyin Liu, Jun Huang, Jiayi Ma, “Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints”, International Journal of Remote Sensing, Vol. 36, No. 18, pp. 4724-4747, 2015.


    会议论文

    [1] Chang Li, Yong Ma, Yuan Gao, Zhongyuan Wang, Jiayi Ma, “Sparse Unmixing of Hyperspectral Data based on Robust Linear Mixing Model”, IEEE International Conference on Visual Communications and Image Processing, pp. 1-4, 2016.

    [2] Yong Ma, Chang Li, Jiayi Ma, “Robust Sparse Unmixing of Hyperspectral Data”, IEEE International Geoscience and Remote Sensing Symposium, pp. 6193-6196, 2016.

    [3] Jiayi Ma, Chang Li, Yong Ma, and Zhongyuan Wang, “Hyperspectral Image Denoising Based on Low-Rank Representation and Superpixel Segmentation”, IEEE International Conference on Image Processing, pp. 3086-3090, 2016.

    [4] Jiayi Ma, Junjun Jiang, Chang Li, “Hyperspectral Image Denoising with Segmentation-based Low Rank Representation”, IEEE International Conference on Visual Communications and Image Processing, pp. 1-4, 2016.

    [5] Jiayi Ma, Junjun Jiang, Jun Chen, Chengyin Liu, Chang Li, “Multimodal Retinal Image Registration Using Edge Map and Feature Guided Gaussian Mixture Model”, IEEE International Conference on Visual Communications and Image Processing, pp. 1-4, 2016.

  • 专著教材

  • 申请专利

    [1] 李畅,刘羽,成娟,宋仁成,陈强,彭虎. 一种逐波段广义双线性高光谱图像解混模型和方法. 中国发明专利,专利申请号:201811097454.X

    [2] 陈勋,陶威,李畅,成娟,刘爱萍,刘羽. 噪声环境下基于鲁棒压缩感知的多通道脑电信号重构方法.中国发明专利,专利申请号:201811398547.6

    [3] 刘羽,张超,陈勋,成娟,李畅,宋仁成,基于非下采样轮廓波变换和卷积神经网络的骨龄评估方法,中国发明专利,专利申请号:201810965998.7
  • 获奖成果

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