We develop an algorithm for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. While existing methods focus on either image-level or patch-level sparse regularization strategies, we present a regularization framework that uses both image and patch-level sparsity constraints. The proposed regularization enforces image-level sparsity in terms of spatial finite d...

The SENSE model with sparsity regularization acts as an unconstrained minimization problem to reconstruct the MRI, which obtain better reconstruction results than the traditional SENSE. To implement the sparsity constraints, discrete wavelet transform (DWT) and total variation (TV) are common exploited together to sparsify the MR image. In this paper, a novel sparsifying transform based on the ...

Abstract. In context of document classification, where in a corpus of documents their label tags are readily known, an opportunity lies in utilizing label information to learn document representation spaces with better discriminative properties. To this end, in this paper application of a Variational Bayesian Supervised Nonnegative Matrix Factorization (supervised vbNMF) with label-driven spars...

Diversity measures exploited by blind source separation (BSS) methods are usually based on either statistical attributes/geometrical structures or sparse/overcomplete (underdetermined) representations of the signals. This leads to some inefficient BSS that derived from a mixing matrix (mm), sparse weight vectors (sw), code (sc). In contrast, proposed efficient method, spatiotemporal (ssBSS), av...

In this article,we study the linear time-invariant state-feedback controller design problem for distributed systems. We follow recently developed system level synthesis (SLS) approach and impose locality structure on resulting closed-loop mappings; corresponding implementation inherits prescribed structure. contrast to existing SLS results, we derive an <italic xmlns:mml="http://www.w3.org/1998...

Assuming sparsity or compressibility of the underlying signals, compressed sensing or compressive sampling (CS) exploits the informational efficiency of under-sampled measurements for increased efficiency yet acceptable accuracy in information gathering, transmission and processing, though it often incurs extra computational cost in signal reconstruction. Shannon information quantities and theo...