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方发明

职称:

直属机构: 计算机科学与技术学院

学科:

10 访问

相关教师

个人资料

  • 部门: 计算机科学与技术学院
  • 性别:
  • 专业技术职务: 教授
  • 毕业院校: 华东师范大学
  • 学位: 工学博士
  • 学历: 博士研究生
  • 联系电话: +86-021-62233560
  • 电子邮箱: fmfang@cs.ecnu.edu.cn
  • 办公地址: 中北校区理科大楼B807
  • 通讯地址: 上海市普陀区中山北路3663号东师范大学计算机科学与技术系,邮编:200062
  • 邮编:
  • 传真:

教育经历

工作经历

(1) 2019-12至现在, 华东师范大学, 计算机科学与技术学院, 教授

(2) 2018-8至2018-9, 香港中文大学, 数学系, 访问学者

(3) 2017-7至2017-8, 香港浸会大学, 数学系, 访问学者

(4) 2016-12至2019-12, 华东师范大学, 计算机科学与技术系, 副教授

(5) 2016-7至2016-8, 香港浸会大学, 数学系, 访问学者

(6) 2013-7至2016-12, 华东师范大学, 计算机科学技术系, 讲师

个人简介

方发明,博士,华东师范大学计算机科学与技术学院视觉与机器智能研究所副所长、教授、博士生导师,上海市“晨光学者”。2013年6月于华东师范大学计算机系获工学博士学位。博士毕业论文被评为“华东师范大学优秀学位论文”以及“上海市优秀学位论文”。2013年7月起,加入华东师范大学计算机系。

主要研究方向为机器学习、图像处理。围绕遥感/医学图像恢复、增强、识别、以及三维重建等展开理论和应用研究。工作受到国家自然科学基金重点、面上、NSFC-RGC、上海市“晨光计划”、上海市自然科学基金等8项纵向基金支持;并主持多项企事业单位联合项目。相关成果发表在国际顶级杂志/会议上(共43篇,第一通讯作者28篇,中科院1区/CCFA 17篇,发表期刊会议主要包括:IEEE TIP、TNNLS、TMM、TGRS、TVCG、TCSVT、NeurIPS、CVPR、ECCV等)。任IEEE TIP、TGRS、TMM、TVCG、CVPR、ICCV、AAAI等多个期刊与会议的审稿人和程序委员。培养的博士生/研究生在多项国际顶级赛事中获奖。毕业生去向包括微软、商汤等知名AI企业,以及国内外知名高校。


社会兼职

任IEEE TIP、TGRS、TMM、TVCG、CVPR、ICCV、AAAI等多个期刊与会议的审稿人和程序委员。

研究方向

  • 图像处理(Image Processing)

  • 机器学习(Machine Learning)

开授课程

科研项目

主持代表性项目:

  

1. 上海市科学技术委员会,20ZR1416200,基于多源遥感数据的亚热带森林树种分类与识别算法研究,2020-072023-06,主持

2. 国家自然科学基金委员会,面上项目,61871185,遥感图像快速拼接模型与算法研究,2019-012022-12,主持

3. 上海市教育委员会,上海市教育发展基金会,上海市晨光计划项目,17CG25,基于深度学习的快速拼接模型与算法研究,2018-012019-12,主持

4. 国家自然科学基金委员会,青年项目,61501188,高光谱图像稀疏解混模型及其快速算法研究,2016-012018-12,主持

5. 上海市科学技术委员会,15ZR1410200,基于变分法的遥感图像雾霾去除技术研究,2015-012017-12,主持

  

参与项目:

  

1.国家自然科学基金委员会,重点项目,61731009,面向大数据的快速磁共振成像,2018-012022-12,参加

2.国家自然科学基金委员会,国际(地区)合作与交流项目,61961160734,基于生成对抗学习的磁共振图像增强模型与算法研究,2020-012023-12,参加

  

学术成果

代表性论文:


  1. L. Chen, J. Zhang, S. Lin, F. Fang, J. Ren, “Blind Deblurring for Saturated Images”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (CCF A)

  2. L. Chen, J. Zhang, J. Pan, S. Lin, F. Fang, J. Ren, “Learning a Non-blind Deblurring Network for Night Blurry Images”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (CCF A)

  3. Y. Yuan, F. Fang, and G. Zhang, “Superpixel based Seamless Image Stitching for UAV Images,” IEEE Transactions on Geoscience and Remote Sensing (TGRS)59(2):1565-1576, 2021.(SCI一区)

  4. F. Fang, J. Li, Y. Yuan, T. Zeng and G. Zhang, “Multilevel Edge Features Guided Network for Image Denoising,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020. (SCI一区)

  5. J. Li, J. Li, F. Fang, F. Li and G. Zhang, “Luminance-aware Pyramid Network for Low-light Image Enhancement,” IEEE Transactions on Multimedia (TMM), 2020. (SCI一区)

  6. J. Li, F. Fang, J. Li, K. Mei and G. Zhang, “MDCN: Multi-scale Dense Cross Network for Image Super-Resolution,” IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020. (SCI二区)

  7. F. Fang, J. Li, T. Zeng, “Soft-Edge Assisted Network for Single Image Super-Resolution”, IEEE Transactions on Image Processing (TIP), vol. 29, pp. 4656-4668, 2020. (CCF A)

  8. F. Fang, T. Wang, T. Zeng and G. Zhang, “A Superpixel-Based Variational Model for Image Colorization,” IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 26, no. 10, pp. 2931-2943, 2020. (CCF A)

  9. Z. Xu, T. Wang, F. Fang, Y. Shen, G. Zhang. “Stylization-Based Architecture for Fast Deep Exemplar Colorization”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 9363-9372. (CCFA)

  10. F. Fang, T. Wang, S. Wu, and G. Zhang, “Removing Moire Patterns from Single Images”, Information Sciences, vol. 514, pp. 56–70, 2020. (SCI一区)

  11. F. Fang, T. Wang, Y. Wang, T. Zeng, and G. Zhang, Variational Single Image Dehazing for Enhanced Visualization, IEEE Transactions on Multimedia (TMM)vol. 22, no. 10, pp. 2537-2550, 2020. (SCI一区)

  12. Z. Gu, F. Li, F. Fang, and G. Zhang, “A Novel Retinex-based Fractional-order Variational Model for Images with Severely Low Light”, IEEE Transactions on Image Processing (TIP), vol. 29, pp. 3239-3253, 2020. (CCF A)

  13. L. Chen, F. Fang, J. Liu, G. Zhang, “OID: Outlier Identifying and Discarding in Blind Image Deblurring”, The European Conference on Computer Vision (ECCV), 598-613, 2020. (CCF B)

  14. L. Chen, F. Fang, S. Lei, F. Li, and G. Zhang, “Enhanced Sparse Model for Blind Deblurring,” The European Conference on Computer Vision (ECCV), 631–646, 2020. (CCF B)

  15. H. Zhen, F. Fang, and G. Zhang, “Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction”, 33rd Conference on Neural Information Processing Systems (NeurIPS2019), 2019. (CCF A)

  16. L. Chen, F. Fang, T. Wang, and G. Zhang, “Blind Image Deblurring with Local Maximum Gradient Prior,” IEEE Conference on Computer Vision and Pattern Recognition 2019 (CVPR 2019), pp. 1742-1750, 2019. (CCF A)

  17. T. Wang, F. Fang, F. Li, and G. Zhang, “High Quality Bayesian Pansharpening,” IEEE Transactions on Image Processing (TIP), vol. 28, no. 1, pp. 227-239, 2019. (CCF A)

  18. J. Li, F. Fang, K. Mei, and G. Zhang, “Multi-scale Residual Network for Image Super-Resolution,” The European Conference on Computer Vision (ECCV), pp. 517-532, 2018. (CCF B)

  19. Faming Fang, Fang Li, and Tieyong Zeng,Reducing spatially varying out-of-focus blur from natural image, Inverse Problems and Imaging,11(1),65-85,2017.

  20. Dehao Shang, Tingting Wang, and Faming Fang,Single image dehazing using holder coefficient, in 2016 International Conference on Knowledge Science, Engineering and Management. Passau, Germany: Springer, 314-324, 2016.

  21. Guixu Zhang, Yingying Xu, and Faming Fang, Framelet based sparse unmixing of hyperspectral images, IEEE Transaction on Image Processing, 25(4), 1516-1529,2016.

  22. Fang Li, Faming Fang, and Guixu Zhang,Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation, International Journal of Remote Sensing, 37, 3232-3254, 2016.

  23. Yingying Xu, Faming Fang, and Guixu Zhang,Similarity-guided and Lp-regularized sparse unmixing of hyperspectral data, IEEE Geoscience and Remote Sensing Letters, 12, 2311-2315,2015.

  24. Yang Xiao, Faming Fang, Qian Zhang, Aimin Zhou,and Guixu Zhang, Parameter selection for variational pan-sharpening by using evolutionary algorithm, Remote Sensing Letters, 6, 458-467,2015.

  25. Guixu Zhang, Faming Fang, Fang Li, and Chaomin Shen,Pan-sharpening of multi-spectral images using a new variational model, International Journal of Remote Sensing, 36(5), 1484-1508,2015.

  26. Chunzhi Li, Aimin Zhou, Guixu Zhang, and Faming Fang, An antinoise method for hyper-spectral unmixing, IEEE Geoscience and Remote Sensing Letters, 12(3), 1484-1508,2015. 

  27. Shizhang Tang, Faming Fang, and Guixu Zhang, Variational approach for multi-source image fusion, IET Image Processing, 9, 134-141,2015.

  28. Faming Fang, Fang Li and Tieyong Zeng*. Single image dehazing and denoising: A fast variational approach, SIAM Journal on Imaging Sciences, 7(2), 969–964, 2014.

  29. Faming Fang, Guixu Zhang, Fang Li and Chaomin Shen. Framelet based pan-sharpening via a variational method. Neurocomputing, 129, 362–377, 2014.

  30. Chunzhi Li, Faming Fang, Aimin Zhou, and Guixu Zhang, A novel blind spectral unmixing method based on error analysis of linear mixture model, IEEE Geoscience and Remote Sensing Letters, 11(7), 1180-1184,2014.

  31. Faming Fang, Fang Li, Chaomin Shen and Guixu Zhang. A variational approach for pan-sharpening, IEEE Transactions on Image Processing, 22(7), 2822-2834, 2013.

  32. Faming Fang, Fang Li, Guixu Zhang and Chaomin Shen. A variational method for multisource remote-sensing image fusion. International Journal of Remote Sensing, 34, 2470–2486, 2013.

  33. Huiyan Liu,Fengxia Yan,Jubo Zhu,and Faming Fang, Adaptive vectorial total variation models for multi-channel SAR images despeckling with fast algorithms, IET Image Processing,7(9), 795–804,2013.

  34. Huiyan Liu, Jiying Liu, Fengxia Yan, Jobo Zhu, and Faming Fang,Spatially adapted total variational model for synthetic aperture radar image despeckling, Journal of Electronic Imaging,22(3), 033019, 2013. 

   

  

发明专利:

  1. 方发明,罗小伟,林福辉.“笑脸检测方法及其系统”, 2013.9, 中国, ZL201010276313.1.

  


荣誉及奖励