Data-driven learning of a union of sparsifying transforms model for blind compressed sensing
S Ravishankar, Y Bresler - IEEE Transactions on Computational …, 2016 - ieeexplore.ieee.org
Compressed sensing is a powerful tool in applications such as magnetic resonance imaging
(MRI). It enables accurate recovery of images from highly undersampled measurements by …
(MRI). It enables accurate recovery of images from highly undersampled measurements by …
Undersampled MR Image Reconstruction with Data‐Driven Tight Frame
Undersampled magnetic resonance image reconstruction employing sparsity regularization
has fascinated many researchers in recent years under the support of compressed sensing …
has fascinated many researchers in recent years under the support of compressed sensing …
Reference guided CS-MRI with gradient orientation priors
The theory of Compressed sensing (CS) provides a systematic framework for MR image
reconstruction from under-sampled k-space data. However, severe aliasing artifacts still …
reconstruction from under-sampled k-space data. However, severe aliasing artifacts still …