方发明 - 华东师范大学 - 计算机科学与技术学院

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。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50余篇，第一通讯作者33篇，中科院1区/CCFA 24篇，发表期刊会议主要包括：IEEE TIP、TNNLS、TMM、TGRS、TVCG、TCSVT、NeurIPS、CVPR、ICCV、ECCV等）。担任Frontiers in Plant Science副主编（Associate Editor），担任多个A类会议（CVPR、ICCV等）程序委员会委员，担任10多个国际顶级期刊（IEEE TIP、TMM、TVCG 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。工作经历 2019-12至现在, 华东师范大学, 计算机科学与技术学院, 教授 2018-8至2018-9, 香港中文大学, 数学系, 访问学者 2017-7至2017-8, 香港浸会大学, 数学系, 访问学者 2016-12至2019-12, 华东师范大学, 计算机科学与技术系, 副教授 2016-7至2016-8, 香港浸会大学, 数学系, 访问学者 2013-7至2016-12, 华东师范大学, 计算机科学技术系, 讲师

研究领域

图像处理 (Image Processing) 机器学习 (Machine Learning)

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

T. Wang, F. Fang, H. Zheng, and G. Zhang, “FrMLNet: Framelet-Based Multilevel Network for Pansharpening”, IEEE Transactions on Cybernetics, 2022. 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. 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. Y. Ru, F. Li, F. Fang, G. Zhang, “Patch-based weighted SCAD prior for compressive sensing”, Information Sciences, vol. 592, pp. 137-155, 2022. 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. 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. 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. Q. Dai, F. Fang, J. Li, G. Zhang and A. Zhou, “Edge-guided Composition Network for Image Stitching”, Pattern Recognition, vol. 118, 108019, 2021. 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. 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. 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. 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. 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. 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. 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. 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. 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. F. Fang, T. Wang, S. Wu, and G. Zhang, “Removing moire patterns from single images”, Information Sciences, vol. 514, pp. 56–70, 2020. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

学术兼职

Frontiers in Plant Science副主编（Associate Editor）任多个A类会议（CVPR、ICCV等）程序委员会委员任10多个国际顶级期刊（IEEE TIP、TMM、TVCG 等）审稿人