张凯兵，1975年12月生，男，模式识别与智能系统专业工学博士，信息与通信工程学科博士后，悉尼科技大学访问学者。担任IEEE Signal Processing Letters、Information Sciences、IEEE Transactions on Cybernetics Pattern Recognition、IEEE Trans. on Image Processing等多个国际期刊的审稿人。近5年来，在IEEE TIP、IEEE TNNLS、NeuralNetworks、Neurocomputing、Signal Processing(Elsevier)、Applied Soft Computing、Applied Intelligence、CVPR和ICIP等国际期刊和会议发表论文40余篇，Google Scholar引用3200余次，单篇引用602次，ESI高被引论文5篇。承担国家自然科学基金面项目2项、中国博士后科学基金特等和一等资助各1项、陕西省自然科学基金重点研发计划1项。获陕西省科学技术奖一等奖，教育部高等学校科学研究优秀成果奖二等奖，陕西省高等学校科学技术一等奖。获评2014年度西安电子科技大学优秀博士论文，2018年度ACM西安“新星奖”（排名第一）和ACM中国“新星奖”提名，2019年度“香港桑麻奖教金”, 2019-2020年度拼搏体育官网入口“师德先进个人”。曾指导员工获全国老员工软件设计大赛一等奖，第十三届中国研究生电子设计竞赛西北赛区三等奖，第十六届中国研究生电子设计竞赛西北赛区二等奖。在2016年开始指导硕士研究生，其中5名研究生获拼搏体育官网入口优秀硕士论文，4名研究生获得研究生创新基金项目，4名研究生获得国家奖学金，3名研究生进入西安交通大学攻读博士研究生。
主要研究方向：影像超分辨重建及质量评价、复杂环境下的计算机视觉检测与分析（弱光图像增强、微小目标检测、运动去模糊）、跨模态人脸合成与识别、智能视频分析与理解、深度学习与模型压缩、跨域自适应迁移学习等。
主持的主要科研项目：
1.基于分治策略与增量字典学习的图像超分辨重建方法研究(国家自然科学基金面上项目, 2020.1—2023.12，经费65万）
2.基于多视角特征集成学习的图像超分辨重建方法（陕西省自然科学基础研究计划重点项目，2018.1—2020.12，经费10万）
3.资源受限环境下实时超分辨重建方法研究(国家自然科学基金面上项目, 2015.1—2018.12，经费81万）
4.基于多线性映射关系学习的实时高质量图像超分辨重建(博士后基金特别资助, 2014.1—2016.6)
5.基于稀疏一致性字典学习超分辨重建方法研究(中国博士后基金一等资助，2014.1—2015.12）
6.基于多视角特征学习的双低油菜缺素智能诊断方法(省自然科学基金, 2016.1—2018.12)
7.多尺度相似性冗余结构学习超分辨重建方法研究(省自然科学基金, 2012.1—2014.12)
8.基于非局部正则化和字典学习超分辨重建方法(省教育厅中青年项目, 2012.1—2013.12)
主要科研成果：
1.层次化超分辨重建方法，2020年度陕西高等学校科学技术奖，一等奖(排序1).
2.基于广义稀疏表示的图像超分辨重建方法，2019年度陕西省电子学会自然科学奖一等奖(排序1).
3.2018年度ACM西安“新星奖”奖（排序1）.
4.复杂纺织品缺陷图像分析及产品开发，2018年陕西省科学技术，一等奖（排序8）
5.异构可视媒体的内容分析与可信服务研究，2015年度陕西省科学技术，一等奖（排序9）
6.2014年西安电子科技大学优秀博士论文.
7.临地空间信息栅格网理论与关键技术, 2013年度高等学校科学研究优秀成果奖（科学技术），二等奖（排序7）.
8.视频监控序列中基于画像的人脸检索，2011年度陕西省高等学校科学技术奖，二等奖（排序7）.
授权专利：
1.张凯兵,王珍,间亚娣,刘秀平,景军锋,苏泽斌,朱丹妮,李敏奇.一种基于多级字典学习的残差实例回归超分辨重建方法.专利号：2018 1 0320484.6,授权公告日: 2021年12月3日.
2.一种基于AdaBoost实例回归的超分辨率重建方法.专利号：2018 1 0320295.9
3.一种基于半监督流形嵌入的人群计数方法.专利号：201911113493.9
4.一种基于主动判别性跨域对齐的低分辨人脸识别方法.专利号：202010465593.4
5.一种基于典型相关分析融合特征的行人再识别方法.专利号：201911114451.7
6.一种基于多流形耦合映射的低分辨人脸识别方法.专利号：201910954656.X
7.一种基于聚类回归的图像超分辨方法.专利号：202010094638.1
8.一种多视觉特征集成的无参考超分辨图像质量评价方法.专利号：202010086336.X
9.基于stacking无参考型超分辨图像质量评价方法.专利号：202010086355.2
10.一种基于Stacking集成学习的图像超分辨方法.专利号：202010052099.5
11.基于级联回归基学习的单帧图像超分辨重建方法.专利号：201810689607.3
12.一种基于耦合判别流形对齐的低分辨人脸识别方法.专利号:202010465414.7
主要期刊论文：
[1]Dongtong Ma(硕士研究生), Kaibing Zhang*, Qizhi Cao, et al. Coordinate Attention Guided Dual-Teacher Adaptive Knowledge Distillation for image classification, Expert Systems With Applications, 2024,250,123892. (SCI:中科院JCR一区Top，IF=8.5 (2023))
[2]Xue Wu(硕士研究生), Kaibing Zhang, Yanting Hu, et al. Multi-scale non-local attention network for image super-resolution, Signal Processing, 2024, 218, 109362. (SCI:中科院JCR一区Top，CCF B类，IF=8.0 (2023))
[3] Youjiang Yu, Kaibing Zhang*（共同一作）, Xiaohua Wang, et al.An Adaptive Region Proposal Network with Progressive Attention Propagation for Tiny Person Detection from UAV Images, IEEE Transactions on Circuits and Systems for Video Technology, 2023, doi: 10.1109/TCSVT.2023.3335157. (SCI:中科院JCR一区Top，CCF B类，IF=8.4 (2023))
[4]Kaibing Zhang,Dongdong Zheng, Jie Li, et al. Coupled discriminative manifold alignment for low-resolution face recognition.Pattern Recognition, 2024,147, 110049. (SCI:中科院JCR一区Top，CCF B类，IF=8.0 (2023))
[5]Kaibing Zhang, Cheng Yu(硕士研究生), Jie Li, et al. Multi-branch and Progressive Network for Low-light Image Enhancement. IEEE Transactions on Image Processing, 2023，32：2295-2308. (SCI:中科院JCR一区Top，CCF A类，IF=10.6 (2023))
[6]Qizhi Cao(硕士研究生), Kaibing Zhang*, Xin He, et al.Be An Excellent Student: Review, Preview, and Correction. IEEE Signal Processing Letters, 2023,30, 1722-1725.
[7]Xing Quan(硕士研究生), Kaibing Zhang, Hui Li, et al.TADSRNet: A triple-attention dual-scale residual networkfor super-resolution image quality assessment. Applied Intelligence, 2023,53:26708–26724
[8]Xing Quan(硕士研究生), Kaibing Zhang, Hui Li, et al.Learning cascade regression for super-resolution imagequality assessment. Applied Intelligence, 2023,53:27304–27322
[9]Chenchen Xi(硕士研究生), Kaibing Zhang, Xin He, et al. Soft-edge-guided significant coordinate attention network for scene text image super-resolution. The Visual Computer, 2023.
[10]Li Hui（硕士研究生）, Zhang Kaibing*, Niu Zhenxing, Shi Hongyu. C2MT: A Credible and Class-Aware Multi-Task Transformer for SR-IQA. IEEE Signal Processing Letters, 2022, 29: 2662-2666.
[11]Yu Youjiang, Yuan Chen, Zhang Kaibing*(张凯兵), et al. A Lightweight Multi-Branch Network for Low-Light Image Enhancement. Electronics Letters, 2023.
[12]Zhang Ting（硕士研究生）, Wang Huake（硕士研究生）, Zhang Kaibing*(张凯兵), et al. Deformable channel non‐local network for crowd counting, Electronics Letters, 2023.
[13]Xin He(硕士研究生）,Kaibing Zhang, Yuhong Zhang, et al. SECANet: A structure-enhanced attention network with dual-domain contrastive learning for scene text image super-resolution, Electronics Letters, 2023.
[14]Wang T, Luo H, Zhang K*(张凯兵), et al. Salient double reconstruction-based discriminative projective dictionary pair learning for crowd counting. Applied Intelligence, 2023, 53(2):1981-1996.
[15]Tao Wang, Ting Zhang(硕士研究生), Zhang Kaibing*. Context Attention Fusion Network for Crowd Counting. Knowledge-Based Systems, 2023,271:110541. (SCI:中科院JCR一区Top，CCF B类，IF=8.139 (2022))
[16]Yan J, Zhang K*(张凯兵), Lou S, et al. Learning graph-constrained cascade regressors for single image super-resolution. Applied Intelligence, 2022, 52(10): 10867-10884.
[17]Luo H（硕士研究生）, Zhang K(张凯兵), Luo S, et al. Locality-Adaptive Structured Dictionary Learning for Cross-Domain Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(4):2425-2440.
[18]Cheng Zhuang（硕士研究生）, Minqi Li, Kaibing Zhang*(张凯兵), et al. Multi-Level Landmark-Guided Deep Network for Face Super-Resolution，Neural Networks, 2022,15：276-286. (SCI:中科院JCR二区，IF=8.05 (2020))
[19]Tingyue Zhang（硕士研究生）, Kaibing Zhang*(张凯兵), Xiao Cui, et al.Joint channel-spatial attention network for super-resolution image quality assessment, Applied Intelligence, 2022, 52 (15) :17118-17132.
[20]Kaibing Zhang*(张凯兵), Danni Zhu（硕士研究生）, Jie Li, et al. Learning stacking regression for no-reference super-resolution image quality assessment, Signal Processing, 178, 2021:107771.(SCI: 000582425200010)
[21]Wei Liu, HuakeWang（硕士研究生）, Hao Luo, Kaibing Zhang(张凯兵)*, et al. Pseudo-label growth dictionary pair learning for crowd counting, Applied Intelligence, 2021.(SCI: 000640755200003)
[22]Kaibing Zhang (张凯兵)*, Shuang Luo（硕士研究生）, Minqi Li, et al. Learning stacking regressors for single image super-resolution. Applied Intelligence, 2020, 50(12): 4325-4341 (SCI：000550284200001)
[23]Kaibing Zhang(张凯兵)*, Huake Wang H（硕士研究生）, Wei Liu, et al. An efficient semi-supervised manifold embedding for crowd counting. Applied Soft Computing, 2020:106634.(SCI: 000582762000057)
[24]Minqi Li, Richard Yida Xu, Jing Xin, Kaibing Zhang*(张凯兵), Junfeng Jing.Fast non-rigid points registration with cluster correspondences projection, Signal Processing, Signal Processing, 2020, 170:324-337. (SCI:000401981800008)
[25]Minqi Li, Xiangjian He, Richard Yida Xu, Kaibing Zhang*(张凯兵), Junfeng Jing. Face hallucination based on cluster consistent dictionary learning, IET Image Processing, 2021, 15,12: 2841-2853.
[26]Kaibing Zhang (张凯兵), Zhen Wang（硕士研究生）, Jie Li, et al.Learning recurrent residual regressors for single image super-resolution, Signal Processing, 2019, 154:324-337. (SCI:000401981800008)
[27]Kaibing Zhang (张凯兵), Jie Li, Haijun Wang, Xiuping Liu, and Xinbo Gao*, Learning local dictionaries and similarity structures for single image super-resolution, Signal Processing, 2018, 142: 231–243 (SCI: 000412611900025)
[28]Kaibing Zhang (张凯兵), Dacheng Tao, Xinbo Gao,Xuelong Li, and Jie Li , Coarse-to-fine learning for single image super-resolution,IEEE Transactions Neural Networks and Learning Systems,2017, 28(5):1109-1122. (SCI:000401981800008)
[29]Kaibing Zhang(张凯兵)，XinboGao，Jie Li，HongxingXia.Single image super-resolution using regularization of non-local steering kernel regression, Signal Processing, 2016,123: 53-63. (SCI: 000371838800006)
[30]Kaibing Zhang (张凯兵), Dacheng Tao, Xinbo Gao, Xuelong Li, and ZenggangXiong, Learning multiple linear mappings for efficient single image super-resolution, IEEE Transactions on Image Processing,2015, 24(3) 846–861. (SCI:000348458000002)
[31]Kaibing Zhang (张凯兵), Xinbo Gao, Dacheng Tao, and Xuelong Li, Single image super-resolution with multi-scale similarity learning, IEEE Transactions on Neural Networks and Learning Systems,2013, 24(10): 1648-1659. (SCI: 000325981400012, EI: 20134216849774)
[32]Kaibing Zhang (张凯兵), Xinbo Gao, Dacheng Tao, and Xuelong Li, Single image super–resolution with non–local means and steering kernel regression. IEEE Transactions on Image Processing, 2012, 21(11):4544–4556.(SCI:000310140700005, EI:20124415619794)
[33]Kaibing Zhang (张凯兵),Guangwu Mu, Yuan Yuan, Xinbo Gao, and Dacheng Tao, Video superresolution with 3D adaptive normalized convolution, Neurocomputing, 2012, 94:140–151. (SCI:000307087000014, EI: 20122815227441)
[34]Xinbo Gao, Kaibing Zhang (张凯兵),Dacheng Tao, and Xuelong Li, Joint learning for single image super–resolution via a coupled constraint, IEEE Transactions on Image Processing,2012，21,2:2：469–480. (SCI: 000300559700004, EI: 20120514729691)
[35]Xinbo Gao, Kaibing Zhang (张凯兵), Dacheng Tao, and Xuelong Li, Single image super–resolution with sparse neighbor embedding, IEEE Transactions on Image Processing, 2012, 21(7):3194–3205. (SCI: 000305577600007, EI: 20122615154413)
[36]Kaibing Zhang (张凯兵),Xinbo Gao, Xuelong Li, and Dacheng Tao, Partially supervised neighbor embedding for example–based image super–resolution, IEEE Journal of Selected Topics in Signal Processing, 2011, 5:(2): 230–239. (SCI: 000288458100003, EI: 20111313857082)
会议论文：
[1] Ruiqi Tang, Xuejuan Kang,Kaibing Zhang(张凯兵)*, Minqi Li.Multi-scale Feature Mergence Reinforced Network for Person Re-Identification,IEEE International Conference on Artificial Intelligence and Industrial Design,May 28-30,Guangzhou, China, pp.109-113,2021.
[2]Learning a cascade regression for no-reference super-resolution image quality assessment,Proc. IEEE International Conference on Image Processing(ICIP), Sept.22-25,pp. 450-453,Taibai,2019. (EI: 20195207921382)
[3]Kaibing Zhang (张凯兵)*,Xinbo Gao, Dacheng Tao, and Xuelong Li，Multi–scale dictionary for single image super–resolution.Proc. Computer Vision and Pattern Recognition (CVPR),Jun.16–21, Rhode Island, USA, pp.1114–1121.2012.(EI：20124015484215, Acceptance rate= 24%)
[4]Kaibing Zhang (张凯兵)*,Xinbo Gao, Dacheng Tao, and Xuelong Li, Image super-resolution via non-local steering kernel regression regularization.Proc. IEEE International Conference on Image Processing(ICIP), Sep.15–18, pp. 943 – 946, Melbourne, Australia, 2013. (EI: 20141117461493)
[5]Guangwu Mu *, Xinbo Gao,Kaibing Zhang (张凯兵),Xuelong Li, and Dacheng Tao, Single image super resolution with high resolution dictionary.Proc. IEEE International Conference on Image Processing(ICIP),Sep.11–14, pp 1141–1144, Brussels, Belguim, 2011. (EI: 20120514729838)
[6]Kaibing Zhang (张凯兵)*, Jun Lu, Handwritten character recognition via sparse representation and multiple classifiers combination.Proc. IEEE International Conference on Information Theory and Information Security (ICITIS), pp. 1139-1142, 2010. ( EI：20110813683711)
[7]Chunman Yan,Kaibing Zhang (张凯兵), Yunping Qi, Image denoising using modifed nonsubsampled Contourlet transform combined with Gaussian scale mixtures model,Proc. International Conference on Intelligence Science and Big Data Engineering(IScIDE), 2015. (EI: 20155301740838)
[8]Kaibing Zhang (张凯兵)*,Hongxing Xia, Haijun Wang, Chunman Yan, Xinbo Gao, Single image super-resolution with one-pass algorithm and local neighbor regression,Proc. International Conference on Communication Technology,2016,930-935.(EI: 20161502215082)
邮箱：zhangkaibing@xpu.edu.cn
