Results 41 to 50 of about 37,700 (292)
Extended Joint Sparsity Reconstruction for Spatial and Temporal ERT Imaging
Sensors, 2018 Electrical resistance tomography (ERT) is an imaging technique to recover the conductivity distribution with boundary measurements via attached electrodes. There are a wide range of applications using ERT for image reconstruction or parameter calculation
Bo Chen +2 more
doaj +1 more source
PLR-TV: patch-based low rank with spatio-temporal total variation constraints for ungated myocardial perfusion CMR [PDF]
Journal of Cardiovascular Magnetic Resonance, 2014 Background Ungated myocardial perfusion is a promising alternative for simplifying CMR protocols [1]. Spatio-temporal total variation (TV) constrained reconstruction with radial undersampling was used in a pilot study [2]. And TV constraints combined with a low-rank constraint have shown improvement in some cases for gated perfusion imaging [3 ...
Adluru, Ganesh, DiBella, Edward V
openaire +1 more source
The Second Order Shannon Total Generalized Variation for Image Restoration
, 2022 The Supplementary Matlab files of the paper"Alireza Hosseini and Sohrab Bazm, The Second Order Shannon Total Generalized Variation for Image Restoration"Alireza's Email: hosseini.alireza@ut.ac.ir.The matlab codes of deblurring and ...
Hosseini, A (via Mendeley Data)
core +1 more source
An Investigation of Smooth TV-Like Regularization in the Context of the Optical Flow Problem
Journal of Imaging, 2018 Total variation (TV) is widely used in many image processing problems including the regularization of optical flow estimation. In order to deal with non differentiability of the TV regularization term, smooth approximations have been considered in the ...
El Mostafa Kalmoun
doaj +1 more source
TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification
CoRR, 2012 We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms.
Xavier Bresson, Ruiliang Zhang
openaire +2 more sources
Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing
IEEE Access, 2019 Sparse unmixing has attracted much attention in recent years. It aims at estimating the fractional abundances of pure spectral signatures in mixed pixels in hyperspectral images.
Jie Huang +3 more
doaj +1 more source
Guarantees of total variation minimization for signal recovery
, 2013 In this paper, we consider using total variation minimization to recover signals whose gradients have a sparse support, from a small number of measurements.
Xu, Weiyu +3 more
core +1 more source
Results in Physics, 2018 Over the past years, many studies have evaluated the performance of nondestructive testing high-energy X-ray imaging methods. In these high-energy industrial X-ray imaging systems, the noise is very important when accurately assessing the nondestructive ...
Heemoon Cho, Youngjin Lee
doaj +1 more source
Journal of Electronic Science and Technology, 2023 A compressive near-field millimeter wave (MMW) imaging algorithm is proposed. From the compressed sensing (CS) theory, the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.
Jue Lyu +9 more
doaj +1 more source
T1 Over Squared Proton Density Ratio to Characterize Multiple Sclerosis Lesions
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Differentiating remyelinated from demyelinated lesions in MS remains challenging without histological confirmation. This study introduces the T1‐to‐PD2 ratio (TPR) imaging approach and evaluates its ability to characterize MS lesions alongside other quantitative MRI (qMRI) metrics. Methods Thirty individuals with MS (mean age: 47.5 ±
Sarah J. Wright +10 more
wiley +1 more source