受教育经历：
· 1996/9 - 2000/6，中国地质大学，通信工程，学士
· 2001/9 - 2004/6，中国地质大学，计算机应用，硕士
· 2008/9 - 2014/12，武汉科技大学，控制理论与控制工程，博士
主持及参与的科研项目：
[1]国家自然科学基金，基于结构相似性的综合滤波器组优化设计理论及应用(61501337)，2016 - 2018，主持
[2]国家自然科学基金，面向异步电机故障诊断的Synchrosqueezed小波变换理论与算法实现(61471275)，2015 - 2018，参与
代表性论文：
[1] Guo Shiyao, Sheng Yuxia, Chai Li, Zhang Jingxin, PET image reconstruction with kernel and kernel space composite regularizer, IEEE Transactions on Medical Imaging, 2023, doi:10.1109/TMI.2023.3239929.
[2] Yi Liqun, Sheng Yuxia, Chai Li, Zhang Jingxin, Dynamic PET images denoising using spectral graph wavelet transform, Medical & Biological Engineering & Computing, 2022, doi: 10.1007/s11517-022-02698-7
[3] Guo Shiyao, Sheng Yuxia, Chai Li, Zhang Jingxin, Kernel graph filtering - A new method for dynamic sinogram denoising. PLoS ONE, 2021, 16(12): e0260374.
[4] 易利群，盛玉霞，柴利，融合MRI 信息的PET 图像去噪：基于图小波的方法，自动化学报，2021, doi: 10.16383/j.aas.c201036.
[5] 盛玉霞，肖翔，柴利，鼠笼式异步电机转子故障程度诊断方法，控制工程，2021, 28(01):149-154.
[6] 柴利,易静文,盛玉霞，滤波器组框架理论及其在图信号处理中的应用,控制与决策,2018, 33(5):866-878.
[7] Chai Li, Sheng Yuxia, Optimal design of multichannel equalizers for the structural similarity index, IEEE Trans. Image Processing, 2014, 23(12): 5626-5637.
[8] Sheng Yuxia, Chai Li, Zhang Jingxin, Robust optimal post-filter in oversampled lapped transform: theory and application in image coding, Signal Processing, 2013, 93: 2516-2524.
[9] Chai Li, Zhang Jingxin and Sheng Yuxia, Optimal design of oversampled synthesis FBs with lattice structure constraints, IEEE Trans. Signal Processing, 2011, 59(8): 3549-3559.
[10] Sheng Yuxia, Wu Yaru, Yang Liangkang，Reduced-reference image quality assessment for Single-Image Super-Resolution by convolutional neural network. Chinese Control Conference, Hefei, China, July, 2022.
[11] Qin Xiao, Sheng Yuxia, Liu Shiqi, Chai Li, Graph wavelet transform-based denoising for dynamic PET images with multi-feature information. Chinese Control Conference, Hefei, China, July, 2022.
[12] Wu Yaru, Sheng Yuxia, End-to-End reduced-reference quality assessment for single image super-resolution using convolutional network, Chinese Automation Conference, Xiamen, Nov., 2022.
[13] Hui Chengxuan, Sheng Yuxia, Xiong Dan, Chai Li. PET image reconstruction using deep image prior and graph Laplacian regularization. Chinese Control and Decision Conference, Hefei, China, Aug. 2022.
[14] Liu Jiaodi , Sheng Yuxia, Yang Jun, Xiong Dan，Paired dictionary learning based MR image reconstruction from undersampled k-space data，Chinese Control Conference, Shanghai, China, July, 2021.
[15] Yang Zhesen, Sheng Yuxia, Chai Li, Yi Liqun, PET image denoising based on non-local low rank matrix approximation, Chinese Control and Decision Conference, Hefei, China, August, 2020.
[16] Guo Shiyao, Sheng Yuxia, Chai Li, Zhang Jingxin, Graph filtering approach to PET image denoising, International Conference on Industrial Artificial Intelligence(IAI), Shenyang, China, July, 2019.
[17] Yang Liangkang, Sheng Yuxia, Chai Li, A machine learning based reduced-reference image quality assessment method for single-image super-resolution, the 38th Chinese Control Conference, Guangzhou, China, July, 2019.
[18] Liu Tian, Sheng Yuxia, Chai Li, Zhang Jingxin，No-reference stereoscopic image quality algorithm based on features on DCT domain，IEEE Conference on Control Technology and Applications (CCTA)，Hong Kong, China, Aug. 2019.
[19] Zhou Lunxiong, Sheng Yuxia, Chai Li, Zhang Jingxin, SSIM-based weak zero-forcing precoder design for multiuser MIMO system, in Proc. Chinese Control and Decision Conference, Nanchang, China, June, 2019.
[20] Zhang Lu，Sheng Yuxia，Chai Li，SSIM-based optimal non-local means image denoising with improved weighted kernel function, Chinese Control Conference, Dalian, China, Jul. 2017.
授权发明专利：
[1] 盛玉霞，易利群，柴利，一种基于图小波变换的动态PET图像去噪方法和系统，发明专利，2020115352070，授权日期2022.9.30
[2] 盛玉霞，秦潇，柴利，一种回转窑火焰图像频域去噪方法，发明专利，2021101453600，授权日期2022.7.5
[3] 盛玉霞，吴雅儒，柴利，改进加权核函数的非局部均值回转窑火焰图像去噪方法，发明专利，2021101453742，授权日期2022.7.1
[4] 盛玉霞，周伦雄，柴利，基于图像结构相似性的迫零预编码器设计方法，发明专利，2019100618026，授权日期2021.4.2
[5] 柴利，张璐，盛玉霞，一种基于结构相似性的非局部均值去噪优化方法，发明专利，201710392063.X，授权日期2020.5.12
