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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,上海市“启明星”计划获得者。20136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”。20137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(50余篇,第一通讯作者33篇,中科院1/CCFA 24篇,发表期刊会议主要包括:IEEE TIPTNNLSTMMTGRSTVCGTCSVTNeurIPSCVPRICCVECCV等)。担任Frontiers in Plant Science副主编(Associate Editor),担任多个A类会议(CVPRICCV等)程序委员会委员,担任10多个国际顶级期刊(IEEE TIPTMMTVCG 等)审稿人,获2021年度IEEE TMM 最佳审稿人奖 (best reviewer award)。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

  1. Frontiers in Plant Science副主编(Associate Editor

  2. 任多个A类会议(CVPRICCV等)程序委员会委员

  3. 10多个国际顶级期刊(IEEE TIPTMMTVCG 等)审稿人

 

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“启明星计划”,21QA1402500,磁共振图像增强模型与算法研究,2021-07至2024-06

2.    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

3.    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 

4.    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 

5.    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 

6.    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 

  

参与项目:

1.    国家自然科学基金重点项目,61731009,面向大数据的快速磁共振成像,2018.01-2022.12 

2.    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,2020.01-2023.12 

3.    上海市经信委2018人工智能专项,面向宫颈癌预防诊治的“诊验理影”医疗分析云平台及在宫颈癌的示范,2019.01-2020.12


学术成果

代表性论文:

[1]      T. Wang, F. Fang, H. Zheng, and G. Zhang, “FrMLNet: Framelet-Based Multilevel Network for Pansharpening”, IEEE Transactions on Cybernetics, 2022.  

[2]      Q. Yi, J. Li, F. Fang, A. Jiang, G. Zhang, “Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing”, IEEE Transactions on Multimedia (TMM), 2022.  

[3]      Y. Liu, F. Fang, T. Wang, J. Li , Y. Sheng, and G. Zhang, “Multi-Scale Grid Network for Image Deblurring With High-Frequency Guidance”, IEEE Transactions on Multimedia (TMM), 2022. 

[4]      Y. Ru, F. Li, F. Fang, G. Zhang, “Patch-based weighted SCAD prior for compressive sensing”, Information Sciences, vol. 592, pp. 137-155, 2022. 

[5]      P. Lu, F. Fang, H. Zhang, L. Ling and K. Hua. “AugMS-Net: Augmented multiscale network for small cervical tumor segmentation from MRI volumes”, Computers in Biology and Medicine, vol. 141, 104774, 2022.

[6]      Q. Yi, J. Li, Q. Dai, F. Fang, G. Zhang, and T. Zeng, “Structure-Preserving Deraining with Residue Channel Prior Guidance” IEEE International Conference on Computer Vision (ICCV), pp. 4238-4247, 2021. 

[7]      Q. Dai, J. Li, Q. Yi, F. Fang, G. Zhang, “Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation”, 29th ACM International Conference on Multimedia, pp. 1985-1993, 2021. 

[8]      Q. Dai, F. Fang, J. Li, G. Zhang and A. Zhou, “Edge-guided Composition Network for Image Stitching”, Pattern Recognition, vol. 118, 108019, 2021.

[9]      L. Chen, J. Zhang, S. Lin, F. Fang, J. Ren, “Blind Deblurring for Saturated Images”, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6308-6316, 2021. 

[10]   L. Chen, J. Zhang, J. Pan, S. Lin, F. Fang, J. Ren, “Learning a Non-blind Deblurring Network for Night Blurry Images”, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10542-10550 , 2021. 

[11]   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), vol. 32, no. 9, pp. 3956-3970, 2021. 

[12]   Y. Yuan, F. Fang, and G. Zhang, “Superpixel-based Seamless Image Stitching for UAV Images”, IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 59, no. 2, pp. 1565-1576, 2021. 

[13]   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), vol. 23, pp. 3153-3165,2021

[14]   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), vol. 31, no. 7, pp. 2547-2561, 2020. 

[15]   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. 

[16]   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

[17]   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), pp. 9363-9372, 2020. 

[18]   F. Fang, T. Wang, S. Wu, and G. Zhang, “Removing moire patterns from single images”, Information Sciences, vol. 514, pp. 56–70, 2020. 

[19]   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. 

[20]   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. 

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

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

[23]   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.  

[24]   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.  

[25]   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. 

[26]   H. Chen, F. Fang, “Bregman-tanimoto based method for contrast preserving decolorization”, 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 1240-1245, 2019. 

[27]   F. Fang, T. Wang, Y. Fang, G. Zhang, “Fast Color Blending for Seamless Image Stitching”, IEEE Geoscience and Remote Sensing Letters, vol.16, no.7, pp. 1115-1119, 2019.

[28]   J. Liu, F. Fang, N. Du, “Color-to-gray Conversion with Perceptual Preservation and Dark Channel Prior”, International Journal of Numerical Analysis and Modeling, vol.16, no.4, pp.668-679, 2019.

[29]   J. Li, F. Fang, K. Mei, and G. Zhang, “Multi-scale residual network for image super-resolution”, in The European Conference on Computer Vision (ECCV), pp. 517-532, 2018. 

[30]   F. Fang, F. Li, T. Zeng, “Reducing spatially varying out-of-focus blur from natural image”, Inverse Problems and Imaging, vol.11, no.1, pp.65-85, 2017.

[31]   G. Zhang, Y. Xu, F. Fang, “Framelet-based sparse unmixing of Hyperspectral Images”, IEEE Transactions on Image Processing (TIP), Vol. 25, no.4, pp. 1516-1529, 2016. 

[32]   F. Li, F. Fang, G. Zhang, “Unsupervised change detection in SAR images using curvelet and L1-norm based  soft segmentation”, International Journal of Remote Sensing, vol.37, no. 14, pp. 3232-3254, 2016.

[33]    Y. Xu, F. Fang, G. Zhang, “Similarity-Guided and -Regularized Sparse Unmixing of Hyperspectral Data”, IEEE Geoscience and Remote Sensing Letters, vol.12, no.11, pp.2311-2315, 2015.

[34]   G. Zhang, F. Fang, A. Zhou, F. Li, “Pan-sharpening of multi-spectral images using a new variational model”, International Journal of Remote Sensing, vol.36, no.5, pp. 1484-1508, 2015.

[35]   C. Li, A. Zhou, G. Zhang, F. Fang, “An Antinoise Method for Hyperspectral Unmixing”, IEEE Geoscience and Remote Sensing Letters, vol.12, no.3, pp. 636-640, 2015.

[36]   F. Fang, F. Li, T. Zeng, “Single image dehazing and denoising: a fast variational approach”, SIAM Journal on Imaging Sciences, vol.7, no.2, pp. 969-996, 2014.

[37]   F. Fang, G. Zhang, F. Li, C. Shen,“Framelet based pan-sharpening via a variational method”, Neurocomputing, vol. 129, no.1, pp.362-377, 2014.

[38]   C. Li, F. Fang, A. Zhou, G. Zhang, “A Novel Blind Spectral Unmixing Method Based on Error Analysis of Linear Mixture Model”, IEEE Geoscience and Remote Sensing Letters, vol.11, no. 7, pp.1180-1184, 2014.

[39]   F. Fang, F. Li, C. Shen, G. Zhang; “A variational approach for pan-sharpening”, IEEE Transactions on Image Processing, vol.22, no.7, pp. 2822-2834, 2013. 

[40]   F. Fang, F. Li, G. Zhang, C. Shen, “A variational method for multisource remote-sensing image fusion”, International Journal of Remote Sensing, vol.34, no.7, pp. 2470-2486, 2013.

[41]   H. Liu, F. Yan, J. Zhu, F. Fang, “Adaptive vectorial total variation models for multi-channel synthetic aperture radar images despeckling with fast algorithms”, IET Image Processing, vol. 7, no. 9, pp. 795-804, 2013.

[42]   H. Liu, J. Liu, F. Yan, J. Zhu, F. Fang, “Spatially adapted total variational model for synthetic aperture radar image despeckling”, Journal of Electronic Imaging, vol, 22, no. 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