Sparse reconstruction by separable approximation
Finding sparse approximate solutions to large underdetermined linear systems of equations
is a common problem in signal/image processing and statistics. Basis pursuit, the least …
is a common problem in signal/image processing and statistics. Basis pursuit, the least …
Bregman Iterative Algorithms for -Minimization with Applications to Compressed Sensing
We propose simple and extremely efficient methods for solving the basis pursuit problem
\min{‖u‖_1:Au=f,u∈R^n\}, which is used in compressed sensing. Our methods are based …
\min{‖u‖_1:Au=f,u∈R^n\}, which is used in compressed sensing. Our methods are based …
Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit
We demonstrate a simple greedy algorithm that can reliably recover a vector v¿¿ d from
incomplete and inaccurate measurements x=¿ v+ e. Here,¿ is a N xd measurement matrix …
incomplete and inaccurate measurements x=¿ v+ e. Here,¿ is a N xd measurement matrix …
Fixed-Point Continuation for -Minimization: Methodology and Convergence
We present a framework for solving the large-scale \ell_1-regularized convex minimization
problem: \min‖x‖_1+μf(x). Our approach is based on two powerful algorithmic ideas …
problem: \min‖x‖_1+μf(x). Our approach is based on two powerful algorithmic ideas …
Non-parametric seismic data recovery with curvelet frames
Seismic data recovery from data with missing traces on otherwise regular acquisition grids
forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery …
forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery …
Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic -Minimization
In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of
potential benefits ranging from high-temporal-rate observation of physiological processes to …
potential benefits ranging from high-temporal-rate observation of physiological processes to …
Simply denoise: Wavefield reconstruction via jittered undersampling
We present a new, discrete undersampling scheme designed to favor wavefield
reconstruction by sparsity-promoting inversion with transform elements localized in the …
reconstruction by sparsity-promoting inversion with transform elements localized in the …
Accelerated projected gradient method for linear inverse problems with sparsity constraints
Regularization of ill-posed linear inverse problems via ℓ 1 penalization has been proposed
for cases where the solution is known to be (almost) sparse. One way to obtain the minimizer …
for cases where the solution is known to be (almost) sparse. One way to obtain the minimizer …
A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework
Based on the methodological similarity between sparse signal reconstruction and system
identification, a new approach for sparse signal reconstruction in compressive sensing (CS) …
identification, a new approach for sparse signal reconstruction in compressive sensing (CS) …
Image reconstruction for electrical capacitance tomography based on sparse representation
Image reconstruction for electrical capacitance tomography (ECT) is a nonlinear problem. A
generalized inverse operator is usually ill-posed (unbounded) and ill-conditioned (with a …
generalized inverse operator is usually ill-posed (unbounded) and ill-conditioned (with a …