Abstract Compressed sensing based Magnetic Resonance imaging (MRI) via sparse
representation (or transform) has recently attracted broad interest. The tight frame (TF) …

In recent years, most of the studies have shown that the generalized iterated shrinkage
thresholdings (GISTs) have become the commonly used first-order optimization algorithms …

The seismic data acquisition is an indispensable step in seismic exploration, whose cost
takes up a large proportion of seismic exploration. The cost of seismic data acquisition has …

Compressed sensing (CS) is well known method for solving fast reconstruction of magnetic
resonance (MR) images. However, the quality of the reconstructed images is sensitive to the …

A common technique for compressing a neural network is to compute the k-rank ℓ 2
approximation A k of the matrix A∈ R n× d via SVD that corresponds to a fully connected …

Transplantation of pancreatic islets is a possible treatment option for patients suffering from
Type I diabetes. In vivo imaging of transplanted islets is important for assessment of the …

One-bit measurements widely exist in the real world and can be used to recover sparse
signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we …

This paper deals with designing sensing matrix for compressive sensing systems.
Traditionally, the optimal sensing matrix is designed so that the Gram of the equivalent …

Cooperative networks have been proposed as an efficient solution to improve the detection
capability of a cognitive radio system. This type of networks is highly vulnerable to different …

This letter characterizes the proximal operator of the piece-wise exponential function with a
given shape parameter, which is a popular non-convex surrogate of the-norm in support …