Low-rank modeling generally refers to a class of methods that solves problems by
representing variables of interest as low-rank matrices. It has achieved great success in …

Purpose To apply the low‐rank plus sparse (L+ S) matrix decomposition model to
reconstruct undersampled dynamic MRI as a superposition of background and dynamic …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Suppose we have samples of a subset of a collection of random variables. No additional
information is provided about the number of latent variables, nor of the relationship between …

Spectral computed tomography (CT) has recently emerged as an advanced version of
medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two …

Partial separability (PS) and sparsity have been previously used to enable reconstruction of
dynamic images from undersampled (k, t)-space data. This paper presents a new method to …

With pervasive sensors continuously collecting and storing massive amounts of information,
there is no doubt this is an era of data deluge. Learning from these large volumes of data is …

We propose a compressive sensing approach for multi-energy computed tomography (CT),
namely the prior rank, intensity and sparsity model (PRISM). To further compress the multi …

In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+ S) decomposition,
or robust principal component analysis (PCA), has achieved stunning performance …

X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis
and image-guided interventions. In this paper, we propose a new deep learning based …