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Enhanced Sparse Bayesian Learning via Statistical Thresholding for Signals in Structured Noise
(Institute of Electrical and Electronics Engineers, 2013-11)
In this paper we address the problem of sparse signal reconstruction. We propose a new algorithm that determines the signal support applying statistical thresholding to accept the active components of the model. This ...
Synergistic multi-contrast cardiac magnetic resonance image reconstruction
(ROYAL SOC, 2021)
Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in ...
Geological Facies Recovery Based on Weighted l(1)-Regularization
(Springer, 2020)
A weighted compressed sensing (WCS) algorithm is proposed for the problem of channelizetl facies reconstruction from pixel-based measurements. This strategy integrates information from: (i) image structure in a transform ...
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions
(Ieee-inst Electrical Electronics Engineers Inc, 2017-07-01)
Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. ...
Regularized sparse features for noisy speech enhancement using deep neural networks
A speech enhancement algorithm improves the perceptual aspects of a speech degraded by noise signals. We propose a phase-aware deep neural network (DNN) using the regularized sparse features for speech enhancement. A ...
Fast Transforms for Acoustic Imaging-Part I: Theory
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
The classical approach for acoustic imaging consists of beamforming, and produces the source distribution of interest convolved with the array point spread function. This convolution smears the image of interest, significantly ...