lee是个小吃货
在 IEEE TSE、TKDE、TSMCB、TCYB,Evolutionary Computation Journal, 中国科学、科学通报等期刊及ICSE, GECCO 等知名国际会议录用、发表论文60多篇。获2010年GECCO国际会议元启发式算法领域最佳论文提名。自2004年以来,先后担任Communicationsof the ACM、IEEE TSE、TEVC、TSMCB、TCYB,Knowledge and Information Systems、中国科学、计算机学报、自动化学报等国内外20余种期刊审稿人。国家自然科学基金 、教育部博士点基金评审专家及大连市科技局评审专家。担任第25届IEA/AIE国际会议PC chair 及多个国际会议PC。兼任职务:中国计算机学会高级会员、中国计算机学会软件工程专委会委员、中国计算机学会计算机应用专委会委员、Frontiers of Computer Science 青年AE。
亲亲E宝贝
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun [ pdf ]
Blind Super-Resolution With Iterative Kernel Correction Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong [ pdf ]
Camera Lens Super-Resolution Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu [ pdf ], [ supp ]
Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]
Towards Real Scene Super-Resolution With Raw Images Xiangyu Xu, Yongrui Ma, Wenxiu Sun [ pdf ]
ODE-Inspired Network Design for Single Image Super-Resolution Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, Jian Cheng [ pdf ], [ supp ]
Feedback Network for Image Super-Resolution Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, Wei Wu [ pdf ], [ supp ]
Recurrent Back-Projection Network for Video Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ], [ supp ]
Image Super-Resolution by Neural Texture Transfer Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi [ pdf ]
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination Jae Woong Soh, Gu Yong Park, Junho Jo, Nam Ik Cho [ pdf ], [ supp ]
3D Appearance Super-Resolution With Deep Learning Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, Luc Van Gool [ pdf ], [ supp ]
Fast Spatio-Temporal Residual Network for Video Super-Resolution Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, Dacheng Tao [ pdf ], [ supp ]
Residual Networks for Light Field Image Super-Resolution Shuo Zhang, Youfang Lin, Hao Sheng [ pdf ]
Second-Order Attention Network for Single Image Super-Resolution Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, Lei Zhang [ pdf ], [ pdf ]
Hyperspectral Image Super-Resolution With Optimized RGB Guidance Ying Fu, Tao Zhang, Yinqiang Zheng, Debing Zhang, Hua Huang [ pdf ]
Learning Parallax Attention for Stereo Image Super-Resolution Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo [ pdf ], [ supp ]
Face Super-resolution Guided by Facial Component Heatmaps Xin Yu, Basura Fernando, Bernard Ghanem, Fatih Porikli, Richard Hartley [ pdf ]
Image Super-Resolution Using Very Deep Residual Channel Attention Networks Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu [ pdf ]
Super-Resolution and Sparse View CT Reconstruction Guangming Zang, Mohamed Aly, Ramzi Idoughi, Peter Wonka, Wolfgang Heidrich [ pdf ]
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn [ pdf ]
SRFeat: Single Image Super-Resolution with Feature Discrimination Seong-Jin Park, Hyeongseok Son, Sunghyun Cho, Ki-Sang Hong, Seungyong Lee [ pdf ]
To learn image super-resolution, use a GAN to learn how to do image degradation first Adrian Bulat, Jing Yang, Georgios Tzimiropoulos [ pdf ]
Multi-scale Residual Network for Image Super-Resolution Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang [ pdf ]
Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses With GANs Adrian Bulat, Georgios Tzimiropoulos [ pdf ] [ Supp ]
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers [ pdf ] [ Supp ]
Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy [ pdf ]
Fast and Accurate Single Image Super-Resolution via Information Distillation Network Zheng Hui, Xiumei Wang, Xinbo Gao [ pdf ]
Image Super-Resolution via Dual-State Recurrent Networks Wei Han, Shiyu Chang, Ding Liu, Mo Yu, Michael Witbrock, Thomas S. Huang [ pdf ]
Deep Back-Projection Networks for Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ] [ Supp ]
Residual Dense Network for Image Super-Resolution Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu [ pdf ]
FSRNet: End-to-End Learning Face Super-Resolution With Facial Priors Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, Jian Yang [ pdf ]
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution Ying Qu, Hairong Qi, Chiman Kwan [ pdf ]
“Zero-Shot” Super-Resolution Using Deep Internal Learning Assaf Shocher, Nadav Cohen, Michal Irani [ pdf ]
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, Seon Joo Kim [ pdf ] [ Supp ]
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]
Feature Super-Resolution: Make Machine See More Clearly Weimin Tan, Bo Yan, Bahetiyaer Bare [ pdf ]
Frame-Recurrent Video Super-Resolution Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown [ pdf ]
Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light Yuki Shiba, Satoshi Ono, Ryo Furukawa, Shinsaku Hiura, Hiroshi Kawasaki [ pdf ] [ Supp ] [ video ]
Robust Video Super-Resolution With Learned Temporal Dynamics Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang [ pdf ]
Detail-Revealing Deep Video Super-Resolution Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia [ pdf ] [ video ]
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis Mehdi S. M. Sajjadi, Bernhard Scholkopf, Michael Hirsch [ pdf ] [ Supp ][ video ]
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution From a Blurred Image Sequence Haesol Park, Kyoung Mu Lee [ pdf ] [ Supp ]
Image Super-Resolution Using Dense Skip Connections Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao [ pdf ]
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang [ pdf ]
Image Super-Resolution via Deep Recursive Residual Network Ying Tai, Jian Yang, Xiaoming Liu [ pdf ]
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ] [ video ]
Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ]
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization Renwei Dian, Leyuan Fang, Shutao Li [ pdf ] [ poster ]
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding Yawen Huang, Ling Shao, Alejandro F. Frangi [ pdf ] [ poster ]
Reference Guided Deep Super-Resolution via Manifold Localized External Compensation Wenhan Yang, Sifeng Xia, Jiaying Liu, and Zongming Guo Accepted by IEEE Trans. on Circuit System for Video Technology (TCSVT), June 2018. [ project ]
Joint-Feature Guided Depth Map Super-Resolution With Face Priors Shuai Yang, Jiaying Liu, Yuming Fang, and Zongming Guo IEEE Trans. on Cybernetics (TCYB), Vol.48, No.1, pp.399-411, Jan. 2018. [ project ]
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo and Shuicheng Yan IEEE Trans. on Image Processing (TIP), Vol.26, No.12, pp.5895-5907, Dec. 2017. [ project ]
Retrieval Compensated Group Structured Sparsity for Image Super-Resolution Jiaying Liu, Wenhan Yang, Xinfeng Zhang, and Zongming Guo IEEE Trans. on Multimedia (TMM), Vol.19, No.2, pp.302-216, Feb. 2017. [ project ]
ECCV2016 Accelerating the Super-Resolution Convolutional Neural Network Chao Dong, Chen Change Loy, Xiaoou Tang [ project ]
Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson, Alexandre Alahi, Li Fei-Fei [ pdf ]
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks Yifan Wang, Lijun Wang, Hongyu Wang, Peihua Li [ pdf ]
Deeply-Recursive Convolutional Network for Image Super-Resolution Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]
Accurate Image Super-Resolution Using Very Deep Convolutional Networks Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang [ pdf ]
Image Super-Resolution Using Deep Convolutional Networks Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) SRCNN [ project ] [ pdf ] [ supplementary material ]
Neighborhood Regression for Edge-Preserving Image Super-Resolution Yanghao Li, Jiaying Liu, Wenhan Yang and Zongming Guo IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, Apr. 2015. [ project ]
Learning a Deep Convolutional Network for Image Super-Resolution Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang ECCV 2014 SRCNN [ project ] [ pdf ]
Super-Resolution From a Single Image Daniel Glasner, Shai Bagon, Michal Irani ICCV 2009 [ project ] [ pdf ]
Video Super-Resolution Based on Spatial-Temporal Recurrent Neural Networks Wenhan Yang, Jiashi Feng, Guosen Xie, Jiaying Liu, Zongming Guo and Shuicheng Yan Computer Vision and Image Understanding (CVIU), Vol.168, pp.79-92, March. 2018. [ project ]
Video Super-Resolution With Convolutional Neural Networks Armin Kappeler ; Seunghwan Yoo ; Qiqin Dai ; Aggelos K. Katsaggelos IEEE Transactions on Computational Imaging [ pdf ]
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network Wenjia Xu; Guangluan XU; Yang Wang; Xian Sun; Daoyu Lin; Yirong WU IGARSS 2018 - 2018 IEEE International Geoscience and Remote SensingSymposium [ pdf ]
怎么样发表论文: 1、想要发表论文,事先要做的就是写好一篇查重率合格,且具备一定价值的论文,论文查重率的具体要求,要根据想要发表的期刊来定,若为普通期刊,则查重
看你上面的刊期,在职称评定中,是以刊期为准的。如果是5月份的刊期,即使是8月份收到的,也是按5月份算的。
六个发表论文的流程:准备论文、投稿、审核、录用、出刊、上网。 1、准备论文:如果论文已经准备好了,按照论文找合适的期刊就好;如果论文没写好,建议还是先找合适的期
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution Xuecai Hu, H
评职称很多人会选择发表职称论文,这就涉及到了期刊的选择,作者们在学术领域可能非常权威,但是对于发表论文来说,很多人可能还是个小白,有些作者文章是发表了,评职称时