联系方式
E-mail:jing.yu@bjut.edu.cn;电话:010-67396006
附主要科研论文
[1] 彭天奇,禹晶,肖创柏.基于跨尺度低秩约束的图像盲解卷积算法.自动化学报(网络首发)
[2] 青晨,禹晶,肖创柏,段娟.深度卷积神经网络图像语义分割研究进展.中国图象图形学报, 2020, 25(6): 1069-1090.
[3] 汪海龙,禹晶,肖创柏.基于点对相似度的深度非松弛哈希算法,自动化学报(网络首发)
[4] Xiaolin Han,Jing Yu, Jinghao Xue, Weidong Sun. Hyperspectral and Multispectral Image Fusion Using Optimized Twin Dictionaries, IEEE Transactions on Image Processing, 2020, 29, 4709-4720.
[5] Xiaolin Han,Jing Yu, Jiqiang Luo, Weidong Sun. Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning, IEEE Transactions on Geoscience and Remote Sensing, 2019,57(3): 1325-1335.
[6]Jing Yu, Zhenchun Chang, Chuangbai Xiao. Blur Kernel Estimation Using Sparse Representation and Cross-Scale Self-Similarity, Multimedia Tools and Applications, 2019,78(13): 18549-18570.
[7] Mengdi Wang,Jing Yu, Jinghao Xue, Weidong Sun. Denoising of Hyperspectral Images Using Group Low-Rank Representation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(9):4420-4427.
[8] Lei Tong, Bin Qian,Jing Yu, Chuangbai Xiao. Spectral and spatial total-variation-regularized multilayer non-negative matrix factorization for hyperspectral unmixing, Journal of Applied Remote Sensing, 2019, 13(3):036510.
[9] Mengdi Wang,Jing Yu, Jinghao Xue, Weidong Sun. Denoising of Hyperspectral Images Using Group Low-Rank Representation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4420-4427.
[10] 常振春,禹晶,孙卫东.基于稀疏表示和结构自相似性的单幅图像盲解卷积算法,自动化学报, 2017, 43(11): 1908-1919.
[11] 杨国铮,禹晶,肖创柏,孙卫东.基于形态字典学习的复杂背景SAR图像舰船尾迹检测,自动化学报, 2017, 43(10): 1713-1725.
[12] 潘宗序,禹晶,肖创柏,孙卫东.基于自适应多字典学习的单幅图像超分辨率算法,电子学报, 2015, 43(2): 209~216.
[13] 黄慧娟,禹晶,孙卫东.基于多字典稀疏表示的遥感图像亚像元映射,电子学报, 2015, 43(6): 1041~1049.
[14] 赵华夏,禹晶,肖创柏.基于目的性优化及改进直方图均衡化的夜间彩色图像增强,计算机研究与发展, 2015, 52(6): 1424~1430.
[15] 潘宗序,禹晶,肖创柏,孙卫东.基于多尺度非局部约束的单幅图像超分辨率算法,自动化学报, 2014, 40(10): 2233~2244.
[16] 潘宗序,禹晶,肖创柏,孙卫东.基于光谱相似性的高光谱图像超分辨率算法,自动化学报, 2014, 40(12): 2797~2807.
[17] 潘宗序,禹晶,胡少兴,孙卫东.基于多尺度结构自相似性的单幅图像超分辨率算法,自动化学报, 2014, 40(4): 594~603.
[18] Huijuan Huang,Jing Yu, et al. Super-resolution mapping via multi-dictionary based sparse representation, IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2055~2059.
[19] Zongxu Pan,Jing Yu, et al. Super-resolution based on compressive sensing and structural self-similarity for remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(9): 4864~4876.
[20] Xiaolin Han,Jing Yu, Jinghao Xue, Weidong Sun, Spectral Super-resolution for RGB Images Using Class-based BP Neural Networks. Digital Image Computing: Techniques and Applications (DICTA), Canberra, Australia, 2018, 721-727
[21] Mengdi Wang,Jing Yu, Weidong Sun. LRR-based hyperspectral image restoration by exploiting the union structure of spectral space and with robust dictionary estimation, IEEE International Conference on Image Processing, Beijing, China, 2017: 4287-4291.
[22]Jing Yu, Zhenchun Chang, Chuangbai Xiao, Weidong Sun. Blind image deblurring based on sparse representation and structural self-similarity, IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, 2017: 1328-1332.
[23] Mengdi Wang,Jing Yu, Lijuan Niu, Weidong Sun. Unsupervised feature extraction for hyperspectral images using combined low rank representation and locally linear embedding, IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, 2017, 1128-1131.
[24] Guozheng Yang,Jing Yu, Chuangbai Xiao, Weidong Sun. Ship wake detection for SAR images with complex backgrounds based on morphological dictionary learning, IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016: 1896-1900.
[25] Wendgdi Wang,Jing Yu, Weidong Sun. Group-based hyperspectral image denoising using low rank representation. IEEE International Conference on Image Processing, Québec City, Canada, 2015: 1623-1627.
[26] Qingyu Pang,Jing Yu, Weidong Sun. A spectral unmixing method based on wavelet weighted similarity. IEEE International Conference on Image Processing (EI), Québec City, Canada, 2015:1865-1869.
[27] Huijuan Huang,Jing Yu, Weidong Sun. Super-resolution mapping based on multi-dictionary via sparse representation. IEEE International Conference on Acoustics, Speech and Signal Processing (EI), Florence, Italy, 2014: 3523-3527.