Recursive recovery of sparse signal sequences from compressive measurements: A review
In this overview article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from compressive …
algorithms for reconstructing a time sequence of sparse signals from compressive …
MR image reconstruction from highly undersampled k-space data by dictionary learning
Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to
enable accurate reconstruction from undersampled k-space data. Recent CS methods have …
enable accurate reconstruction from undersampled k-space data. Recent CS methods have …
Modified-CS: Modifying compressive sensing for problems with partially known support
We study the problem of reconstructing a sparse signal from a limited number of its linear
projections when a part of its support is known, although the known part may contain some …
projections when a part of its support is known, although the known part may contain some …
Block-based compressed sensing of images and video
A number of techniques for the compressed sensing of imagery are surveyed. Various
imaging media are considered, including still images, motion video, as well as multiview …
imaging media are considered, including still images, motion video, as well as multiview …
Low-dimensional models for dimensionality reduction and signal recovery: A geometric perspective
We compare and contrast from a geometric perspective a number of low-dimensional signal
models that support stable information-preserving dimensionality reduction. We consider …
models that support stable information-preserving dimensionality reduction. We consider …
LS-CS-residual (LS-CS): Compressive sensing on least squares residual
N Vaswani - IEEE Transactions on Signal Processing, 2010 - ieeexplore.ieee.org
We consider the problem of recursively and causally reconstructing time sequences of
sparse signals (with unknown and time-varying sparsity patterns) from a limited number of …
sparse signals (with unknown and time-varying sparsity patterns) from a limited number of …
Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging
Natural signals and images are well known to be approximately sparse in transform
domains such as wavelets and discrete cosine transform. This property has been heavily …
domains such as wavelets and discrete cosine transform. This property has been heavily …
Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging
Sparsity-based approaches have been popular in many applications in image processing
and imaging. Compressed sensing exploits the sparsity of images in a transform domain or …
and imaging. Compressed sensing exploits the sparsity of images in a transform domain or …
Compressive sensing with prior support quality information and application to massive MIMO channel estimation with temporal correlation
In this paper, we consider the problem of compressive sensing (CS) recovery with a prior
support and the prior support quality information available. Different from classical works …
support and the prior support quality information available. Different from classical works …
Data-driven learning of a union of sparsifying transforms model for blind compressed sensing
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 …