Results 41 to 50 of about 19,283,190 (228)
Total variation regularization of multi-material topology optimization [PDF]
, 2018 This work is concerned with the determination of the diffusion coefficient from distributed data of the state. This problem is related to homogenization theory on the one hand and to regularization theory on the other hand.
Clason, Christian +2 more
core +2 more sources
Groupwise Multimodal Image Registration using Joint Total Variation [PDF]
, 2020 In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies.
A Collignon +32 more
core +2 more sources
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization
IEEE Access, 2019 Convex optimization, rather than a non-convex approach, still play important roles in many computer science applications because of its exactness and efficiency.
Tatsuya Yokota, Hidekata Hontani
doaj +1 more source
MRI Super-Resolution using Multi-Channel Total Variation [PDF]
, 2018 This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast.
Ashburner, John +3 more
core +2 more sources
Generalized Hessian-Schatten Norm Regularization for Image Reconstruction
IEEE Access, 2022 Regularization plays a crucial role in reliably utilizing imaging systems for scientific and medical investigations. It helps to stabilize the process of computationally undoing any degradation caused by physical limitations of the imaging process.
Manu Ghulyani +2 more
doaj +1 more source
A Novel Total Variation Model for Low-Dose CT Image Denoising
IEEE Access, 2018 Low-dose computed tomography (LDCT) images are polluted by mottle noise and streak artifacts. To improve LDCT images quality, this paper proposes a novel total variation (NTV) model.
Wenbin Chen +8 more
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 This article presents a novel global gradient sparse and nonlocal low-rank tensor decomposition model with a hyper-Laplacian prior for hyperspectral image (HSI) superresolution to produce a high-resolution HSI (HR-HSI) by fusing a low-resolution HSI (LR ...
Yidong Peng +3 more
doaj +1 more source
Continuum limit of total variation on point clouds [PDF]
, 2014 We consider point clouds obtained as random samples of a measure on a Euclidean domain. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect.
Slepčev, Dejan +1 more
core +2 more sources
Non-Quadratic Distances in Model Assessment
Entropy, 2018 One natural way to measure model adequacy is by using statistical distances as loss functions. A related fundamental question is how to construct loss functions that are scientifically and statistically meaningful.
Marianthi Markatou, Yang Chen
doaj +1 more source
Higher-order total variation approaches and generalisations [PDF]
Inverse Problems, 2019 Over the last decades, the total variation (TV) has evolved to be one of the most broadly-used regularisation functionals for inverse problems, in particular for imaging applications.
K. Bredies, M. Holler
semanticscholar +1 more source