Provably Bounded Dynamic Sparsifying Transform Network for Compressive Imaging.

Compressive imaging (CI) aims to recover the underlying image from the under-sampled observations. Recently, deep unfolded CI (DUCI) algorithms, which unfold the iterative algorithms into deep neural networks (DNNs), have achieved remarkable results. Theoretically, unfolding a convergent iterative algorithm could ensure a stable DUCI algorithm, i.

Compressed sensing MRI based on image decomposition model and group sparsity.

The image representation plays an important role in compressed sensing magnetic resonance imaging (CSMRI). However, how to adaptive sparsely represent images is a challenge for accurately reconstructing magnetic resonance (MR) images from highly undersampled data with noise. In order to further improve the reconstruction quality of the MR image, this paper first proposes tight frame-based group sparsity (TFGS) regularization that can capture the structure information of images appropriately, then TFGS regularization is employed to the image cartoon-texture decomposition model to construct CSMRI algorithm, termed cartoon-texture decomposition CSMRI algorithm (CD-MRI).