54 citations · 248 across the 37 of their papers we have counts for
15 papers · 1 filter
An Unsupervised Deep Learning Method for Multi-coil Cine MRI
Ziwen Ke, Jing Cheng, Leslie Ying +3
Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersamp…
Learning Priors in High-frequency Domain for Inverse Imaging Reconstruction
Zhuonan He, Jinjie Zhou, Dong Liang +2
Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging. Recognizing that the popular dictionary learning…
A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems
Huangxuan Zhao, Ziwen Ke, Ningbo Chen +8
Deconvolution is the most commonly used image processing method to remove the blur caused by the point-spread-function (PSF) in optical imaging systems. While this method has been…
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
Yiling Liu, Qiegen Liu, Minghui Zhang +3
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (…
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction
Siyuan Wang, Junjie Lv, Yuanyuan Hu +3
Compressive sensing is an impressive approach for fast MRI. It aims at reconstructing MR image using only a few under-sampled data in k-space, enhancing the efficiency of the data…
Model Learning: Primal Dual Networks for Fast MR imaging
Jing Cheng, Haifeng Wang, Leslie Ying +1
Magnetic resonance imaging (MRI) is known to be a slow imaging modality and undersampling in k-space has been used to increase the imaging speed. However, image reconstruction from…