11 citations · 15 across the 5 of their papers we have counts for
5 papers
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRI
Yinghao Zhang, Haiyan Gui, Ningdi Yang +1
Joint low-rank and sparse unrolling networks have shown superior performance in dynamic MRI reconstruction. However, existing works mainly utilized matrix low-rank priors, neglecti…
Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problems
Yinghao Zhang, Yue Hu
Low-rank regularization-based deep unrolling networks have achieved remarkable success in various inverse imaging problems (IIPs). However, the singular value decomposition (SVD) i…
Deep unrolling Shrinkage Network for Dynamic MR imaging
Yinghao Zhang, Xiaodi Li, Weihang Li +1
Deep unrolling networks that utilize sparsity priors have achieved great success in dynamic magnetic resonance (MR) imaging. The convolutional neural network (CNN) is usually utili…
T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction
Yinghao Zhang, Peng Li, Yue Hu
The tensor low-rank prior has attracted considerable attention in dynamic MR reconstruction. Tensor low-rank methods preserve the inherent high-dimensional structure of data, allow…
Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LTLR-Net)
Yinghao Zhang, Peng Li, Yue Hu
While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, tensor low-rank models have recently emerged as powerful…