Recursive recovery of sparse signal sequences from compressive measurements: A review

N Vaswani, J Zhan - IEEE Transactions on Signal Processing, 2016 - ieeexplore.ieee.org
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 …

MR image reconstruction from highly undersampled k-space data by dictionary learning

S Ravishankar, Y Bresler - IEEE transactions on medical …, 2010 - ieeexplore.ieee.org
Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to
enable accurate reconstruction from undersampled k-space data. Recent CS methods have …

Modified-CS: Modifying compressive sensing for problems with partially known support

N Vaswani, W Lu - IEEE Transactions on Signal Processing, 2010 - ieeexplore.ieee.org
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 …

Block-based compressed sensing of images and video

JE Fowler, S Mun, EW Tramel - Foundations and Trends® in …, 2012 - nowpublishers.com
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 …

Low-dimensional models for dimensionality reduction and signal recovery: A geometric perspective

RG Baraniuk, V Cevher, MB Wakin - Proceedings of the IEEE, 2010 - ieeexplore.ieee.org
We compare and contrast from a geometric perspective a number of low-dimensional signal
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 …

Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging

S Ravishankar, Y Bresler - SIAM Journal on Imaging Sciences, 2015 - SIAM
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 …

Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging

S Ravishankar, BE Moore… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
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 …

Compressive sensing with prior support quality information and application to massive MIMO channel estimation with temporal correlation

X Rao, VKN Lau - IEEE Transactions on Signal Processing, 2015 - ieeexplore.ieee.org
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 …

Data-driven learning of a union of sparsifying transforms model for blind compressed sensing

S Ravishankar, Y Bresler - IEEE Transactions on Computational …, 2016 - ieeexplore.ieee.org
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 …