张凯兵

作者: 时间:2016-10-25 点击数:

所属学科专业:控制科学与工程

导师简介:

张凯兵,1975年生,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 LettersInformation SciencesIEEE Transactions on Cybernetics Pattern RecognitionIEEE Trans. on Image Processing等多个国际期刊的审稿人。承担国家自然科学基金、中国博士后科学基金、省科技厅、省教育厅科学技术项目以及教育厅优秀中青年创新团队项目的研究。近5年来,在IEEE Trans. on Image ProcessingIEEE Trans. on Neural Networks and Learning SystemsNeurocomputingSignal ProcessingCVPRICIP等国际期刊和会议发表论文20余篇,获陕西省高等学校科学技术和教育部高等学校科学研究优秀成果奖。获评2014年度西安电子科技大学优秀博士论文。曾指导学生获得全国大学生软件设计大赛一等奖。

主要研究方向:影像超分辨分析与重建、计算机视觉检测与分析、深度机器学习

近年来主要科研项目

1.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)

2.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)

3.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)

4.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)

5.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)

6.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)

近年来主要科研成果:

1.基于对偶约束的联合学习超分辨方法,2012. 10,中国, 授权号:ZL 201010298564.x.

2.基于高分辨率字典的稀疏表征图像超分辨重建方法, 2011. 8,中国, 申请号:201110058174.x.

3.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)

4.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).

5.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).

6.2014年西安电子科技大学优秀博士论文.

发表论文:

期刊论文:

[1]Coarse-to-fine learning for single image super-resolution,IEEE Transactions Neural Networks and Learning Systems,2017, 28(5):1109-1122. (SCI::000401981800008,IF=6.108/2016)

[2]Optimized multiple linear mappings for single image super-resolution,Optics Communications,2017. (to be published,SCI, IF=1.588/2016)

[3]Learning local dictionaries and similarity structures for single image super-resolution,Signal Processing,2018,142:231–243 (SCI, IF=3.110/2016)

[4]Single image super-resolution using regularization of non-local steering kernel regression,Signal Processing, 2016,123: 53-63. (SCI, IF=3.110/2016)

[5]Learning Multiple Linear Mappings for Efficient Single Image Super-Resolution,IEEE Transactions on Image Processing,2015, 24(3) 846–861. (SCI:000348458000002,IF=4.828/2016)

[6]Similarity constraints based structured output regression machine: an approach to image super-resolution,IEEE Transactions Neural Networks and Learning Systems,2016,27(2):2472-2485.(SCI:000388919600002, IF=6.108/2016)

[7]Single image super-resolution using Gaussian process regression with dictionary-based sampling and Student-t likelihood,IEEE Transactions on Image Processing,26(7): 3556-3568, 2017. (SCI:000402136500020,IF=4.828/2017)

[8]Image super-resolution using non-local Gaussian process regression,Neurocomputing, 2016,194: 95-106. (SCI: 000376548100010, IF= 3.317/2016)

[9]Single image super-resolution using active-sampling Gaussian process regression,IEEE Transactions on Image Processing,25(2): 935-948, 2015. (SCI:000368938400005,IF=4.828/2016)

[10]Fast single image super-resolution using sparse Gaussian process regression,SignalProcessing, 2017, 134: 52-62. (SCI:000393243800005,IF=3.110/2017)

[11]A unified learning framework for single image super-resolution.IEEE Transactions Neural Networks and Learning Systems,2014, 25(3):780–792. (SCI: 000333098700011, IF=6.108/2017)

[12]Single image super-resolution with multi-scale similarity learning,IEEETransactionsonNeural Networksand Learning Systems,2013,24(10):1648-1659.(SCI: 000325981400012, EI: 20134216849774, IF=6.108/2017)

[13]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,IF=5.301/2016)

[14]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,IF=4.828/2016)

[15]Video superresolution with 3D adaptive normalized convolution,Neurocomputing,2012, 94:140–151. (SCI:000307087000014, EI: 20122815227441, IF= 3.317/2016)

[16]Joint learning for single image super–resolution via a coupled constraint,IEEE Transactions on Image Processing,第21卷,第2期,469–480, 2012. (SCI: 000300559700004, EI: 20120514729691, IF=4.828/2016)

[17]Single image super–resolution with sparse neighbor embedding,IEEE Transactions on Image Processing,2012, 21(7):3194–3205. (SCI: 000305577600007, EI: 20122615154413,IF=4.828/2016)

[18]Zernike–moment–based image super resolution.IEEE Transactions on Image Processing,2011,20(10): 2738–2747. (SCI: 000295008100004, EI: 20113814351126, IF=4.828/2016)

[19]基于HSV空间颜色直方图的油菜叶片缺素诊断[J].农业工程学报, 2016, 32(19):179-187.(EI:20163902855601)

会议论文:

[1]Multi–scale dictionary for single image super–resolution.Proc. Computer Vision and Pattern Recognition (CVPR),Jun.16–21, Rhode Island, USA, pp1114–1121.2012.(EI:20124015484215, Acceptance rate= 24%)

[2]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)

[3]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)

[4]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)

[5]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)

[6]Single image super-resolution with one-pass algorithm and local neighbor regression,Proc.International Conference on Communication Technology,2016,930-935.(EI:20161502215082)

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