Следене
Qiegen Liu
Qiegen Liu
Потвърден имейл адрес: ncu.edu.cn - Начална страница
Заглавие
Позовавания
Позовавания
Година
Accelerated high-dimensional MR imaging with sparse sampling using low-rank tensors
J He, Q Liu, A Christodoulou, C Ma, F Lam, ZP Liang
IEEE TMI, 2016
1352016
A radiomics approach with CNN for shear-wave elastography breast tumor classification
Y Zhou, J Xu, Q Liu, C Li, Z Liu, M Wang, H Zheng, S Wang
IEEE Transactions on Biomedical Engineering 65 (9), 1935-1942, 2018
1152018
X-net: Brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies
K Qi, H Yang, C Li, Z Liu, M Wang, Q Liu, S Wang
Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019
1102019
Adaptive dictionary learning in sparse gradient domain for image recovery
Q Liu, S Wang, L Ying, X Peng, Y Zhu, D Liang
IEEE Transactions on Image Processing 22 (12), 4652-4663, 2013
1072013
Highly undersampled magnetic resonance image reconstruction using two-level Bregman method with dictionary updating
Q Liu, S Wang, K Yang, J Luo, Y Zhu, D Liang
IEEE Transactions on Medical Imaging 32 (7), 1290-1301, 2013
842013
Learning joint-sparse codes for calibration-free parallel MR imaging
S Wang, S Tan, Y Gao, Q Liu, L Ying, T Xiao, Y Liu, X Liu, H Zheng, ...
IEEE transactions on medical imaging 37 (1), 251-261, 2017
652017
GcsDecolor: Gradient Correlation Similarity for Efficient Contrast Preserving Decolorization
DL Q. Liu, P.X. Liu, W. Xie, Y. Wang
IEEE Trans. Image Process., 24 (9), 2889-2904, 2015
632015
Dictionary learning based impulse noise removal via L1–L1 minimization
S Wang, Q Liu, Y Xia, P Dong, J Luo, Q Huang, DD Feng
Signal Processing 93 (9), 2696-2708, 2013
602013
PANDA‐ : Integrating principal component analysis and dictionary learning for fast mapping
Y Zhu, Q Zhang, Q Liu, YXJ Wang, X Liu, H Zheng, D Liang, J Yuan
Magnetic resonance in medicine 73 (1), 263-272, 2015
562015
Reconstruction of magnetic resonance imaging by three‐dimensional dual‐dictionary learning
Y Song, Z Zhu, Y Lu, Q Liu, J Zhao
Magnetic resonance in medicine 71 (3), 1285-1298, 2014
502014
IFR-Net: Iterative feature refinement network for compressed sensing MRI
Y Liu, Q Liu, M Zhang, Q Yang, S Wang, D Liang
IEEE Transactions on Computational Imaging 6, 434-446, 2019
492019
Gabor feature based nonlocal means filter for textured image denoising
S Wang, Y Xia, Q Liu, J Luo, Y Zhu, DD Feng
Journal of Visual Communication and Image Representation 23 (7), 1008-1018, 2012
472012
Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors
Q Liu, Q Yang, H Cheng, S Wang, M Zhang, D Liang
Magnetic resonance in medicine 83 (1), 322-336, 2020
452020
Variable augmented neural network for decolorization and multi-exposure fusion
Q Liu, H Leung
Information Fusion 46, 114-127, 2019
432019
Multi-view mammographic density classification by dilated and attention-guided residual learning
C Li, J Xu, Q Liu, Y Zhou, L Mou, Z Pu, Y Xia, H Zheng, S Wang
IEEE/ACM transactions on computational biology and bioinformatics 18 (3 …, 2020
422020
Augmented Lagrangian-based sparse representation method with dictionary updating for image deblurring
Q Liu, D Liang, Y Song, J Luo, Y Zhu, W Li
SIAM Journal on Imaging Sciences 6 (3), 1689-1718, 2013
412013
Detail-preserving image denoising via adaptive clustering and progressive PCA thresholding
W Zhao, Y Lv, Q Liu, B Qin
IEEE Access 6, 6303-6315, 2017
372017
A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond
W Zeng, J Peng, S Wang, Q Liu
Signal Processing: Image Communication 81, 115701, 2020
352020
Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms
B Qin, M Jin, D Hao, Y Lv, Q Liu, Y Zhu, S Ding, J Zhao, B Fei
Pattern recognition 87, 38-54, 2019
322019
An augmented Lagrangian approach to general dictionary learning for image denoising
Q Liu, S Wang, J Luo, Y Zhu, M Ye
Journal of Visual Communication and Image Representation 23 (5), 753-766, 2012
322012
Системата не може да изпълни операцията сега. Опитайте отново по-късно.
Статии 1–20