暂无分享,去创建一个
Dong Liang | Ziwen Ke | Zhuo-Xu Cui | Wenqi Huang | Yanjie Zhu | Jing Cheng | Sen Jia | D. Liang | Jing Cheng | Ziwen Ke | Zhuoxu Cui | Seng Jia | Yanjie Zhu | Wenqi Huang
[1] Kyoung Mu Lee,et al. Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[2] Jeff Wood,et al. Super‐resolution musculoskeletal MRI using deep learning , 2018, Magnetic resonance in medicine.
[3] Alan C. Bovik,et al. Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.
[4] Daniel Rueckert,et al. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.
[5] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[6] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[7] Leon Axel,et al. Combination of Compressed Sensing and Parallel Imaging for Highly-Accelerated 3 D First-Pass Cardiac Perfusion MRI , 2009 .
[8] Radu Timofte,et al. 2018 PIRM Challenge on Perceptual Image Super-resolution , 2018, ArXiv.
[9] Dong Liang,et al. DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training , 2018, NMR in biomedicine.
[10] Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.
[11] Debiao Li,et al. Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network , 2018, MICCAI.
[12] Leslie Ying,et al. Dynamic magnetic resonance imaging using compressed sensing with self-learned nonlinear dictionary (NL-D) , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[13] S. Frick,et al. Compressed Sensing , 2014, Computer Vision, A Reference Guide.
[14] Yifan Wu,et al. Enhanced generative adversarial network for 3D brain MRI super-resolution , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[15] M. Bernstein,et al. Effect of windowing and zero‐filled reconstruction of MRI data on spatial resolution and acquisition strategy , 2001, Journal of magnetic resonance imaging : JMRI.
[16] Chi-Hieu Pham,et al. Brain MRI super-resolution using deep 3D convolutional networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[17] Michael Elad,et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA , 2014, Magnetic resonance in medicine.
[18] Feng Shi,et al. Brain MRI super resolution using 3D deep densely connected neural networks , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[19] Daniel K Sodickson,et al. Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components , 2015, Magnetic resonance in medicine.
[20] Hongming Shan,et al. MRI Super-Resolution With Ensemble Learning and Complementary Priors , 2020, IEEE Transactions on Computational Imaging.
[21] Daniel Rueckert,et al. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.
[22] Bernard Ghanem,et al. ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.