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胡靖

2017-02-21


姓名:胡靖 工学博士,讲师

Email: lesley123@cuit.edu.cn

科研和研究生招生方向:

1、图像处理与计算成像

2、人工智能及数据挖掘


个人简介 胡靖博士2015年1月毕业于清华大学控制科学与工程专业,获工学博士学位,2009年7月毕业于电子科技大学自动化学院获工学学士学位。2015年2月入职成都信息工程大学,现为计算机学院讲师。2016-2017在美国范德比特大学图像科学研究所(VUIIS, Vanderbilt University Institute of Imaging Science, Vanderbilt University)从事功能磁共振分析处理技术研究。

胡靖博士承担和参与包括国家自然科学基金、四川省科技厅等国家和省部级科研项目多项,发表科研论文近十篇,其中SCI检索3篇。

目前承担科研项目:

1. 国家自然科学基金青年基金项目,“含噪图像超分辨率算法研究”(61602065)。

2. 四川省科技支撑计划项目,“基于自学习的单帧图像超分辨率重建研究”(2017RZ0013)。

3. 四川省教育厅重点项目,“含噪视频超分辨率重建算法研究”

4. 成都信息工程大学校中青年学术带头人科研基金,“图形图像处理技术研究”(J201608)

5. 四川省图形图像智能系统协同中心开放基金重点项目,“基于自相似特性的医学图像超分辨率重建研究”(762001009)。


目前授课:

“计算机操作技能”, 本科生.

近5年发表论文(*为通信作者)

1. Jing Hu, Baxter P. Rogers, Xi Wu, et al. Characterization of structural and functional connectivity in epilepsy by integrating diffusion and functional tensor imaging. ISMRM 2017 (accepted).

2. Jing Hu, Baxter P. Rogers, Xi Wu, et al. Structural and functional connectivity in white matter: combined diffusion and functional tensor imaging of epilepsy. ISMRM workshop on Quantitative MRI in White Matter Disorders: Useful, Usable, Used? (accepted).

2. Jing Hu*, Yanfang Wang, Jiliu Zhou. Regression-based single image super-resolution via adaptive patches, ICSIP, 2016, Accepted.

3. Jing Hu, Xi Wu*, Jiliu Zhou. Single Image Super Resolution of 3D MRI Using Local Regression and Intermodality Priors, ICDIP, 2016, Accepted.

4. 胡靖*, 吴锡, 周激流. 基于边缘变化率的无参考模糊图像质量评价方法,NCIG,2016.

5. Jing Hu*, Yupin Luo. Noise-robust video super-resolution using an adaptive spatial-temporal filter. Multimedia Tools and Applications, 2015, 74(21): 9259-9278.(SCI, IF = 1.331)

6. Jing Hu*, Yupin Luo. Single-image superresolution based on local regression and nonlocal self-similarity. Journal of Electronic Imaging, 2014, 23(3): 033014-033014-14. (SCI, IF = 0.616)

7. Jing Hu*, Yupin Luo. Non-local means algorithm with adaptive patch size and bandwidth. International Journal for Light and Electron Optics, 2013, 124(22):5639-5645. (SCI, IF = 0.742)




Curriculum Vitae

Dr. Jing Hu

Lecturer

Department of Computer Science

Chengdu University of Information Technology

Contact: lesley123@cuit.edu.cn


Dr. Jing Hu is a currently a lecturer in the Department of Computer Science, Chengdu University of Information Technology. She received her B.Eng. degree from University of Electronic Science and Technology of China, Chengdu, in 2009, and the Ph. D degree in Control Science and Engineering from Tsinghua University, Beijing, in 2015. From 2016 to 2017, she is a visiting scholar at Vanderbilt University Institute of Imaging Science, Vanderbilt University.


Research interests:

1. Image processing and computational imaging

2. Artificial intelligence and data mining


Courses:

Computer Operation Skills for undergraduate and graduate


Publications (past 5 years):

1. Jing Hu, Baxter P. Rogers, Xi Wu, et al. Characterization of structural and functional connectivity in epilepsy by integrating diffusion and functional tensor imaging. ISMRM 2017 (accepted).

2. Jing Hu, Baxter P. Rogers, Xi Wu, et al. Structural and functional connectivity in white matter: combined diffusion and functional tensor imaging of epilepsy. ISMRM workshop on Quantitative MRI in White Matter Disorders: Useful, Usable, Used? (accepted).

3. Jing Hu*, Yanfang Wang, Jiliu Zhou. Regression-based single image super-resolution via adaptive patches, ICSIP, 2016, Accepted.

4. Jing Hu, Xi Wu*, Jiliu Zhou. Single Image Super Resolution of 3D MRI Using Local Regression and Intermodality Priors, ICDIP, 2016, Accepted.

5. Jing Hu*, Xi Wu, Jiliu Zhou. No-reference blurred image quality assessment based on edge change rate. NCIG,2016.

6. Jing Hu*, Yupin Luo. Noise-robust video super-resolution using an adaptive spatial-temporal filter. Multimedia Tools and Applications, 2015, 74(21): 9259-9278.(SCI, IF = 1.331)

7. Jing Hu*, Yupin Luo. Single-image superresolution based on local regression and nonlocal self-similarity. Journal of Electronic Imaging, 2014, 23(3): 033014-033014-14. (SCI, IF = 0.616)

8. Jing Hu*, Yupin Luo. Non-local means algorithm with adaptive patch size and bandwidth. International Journal for Light and Electron Optics, 2013, 124(22):5639-5645. (SCI, IF = 0.742)