Results 21 to 30 of about 12,292,225 (304)
Simultaneous Image Registration and Monocular Volumetric Reconstruction of a fluid flow [PDF]
, 2011 We propose to combine image registration and volumetric reconstruction from a monocular video of a draining off Hele-Shaw cell filled with water. A Hele-Shaw cell is a tank whose depth is small (e.g. 1 mm) compared to the other dimensions (e.g.
Brunet, Florent +5 more
core +1 more source
Hierarchical Bayesian sparse image reconstruction with application to MRFM [PDF]
, 2008 This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally
Hero, Alfred O. +2 more
core +1 more source
On Hallucinations in Tomographic Image Reconstruction
IEEE Transactions on Medical Imaging, 2021 Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property. Recently, deep neural networks have been actively investigated for regularizing image reconstruction problems by learning a prior for the object ...
Sayantan Bhadra +3 more
openaire +6 more sources
Super-resolution of remotely sensed images with variable-pixel linear reconstruction [PDF]
, 2007 This paper describes the development and applications of a super-resolution method, known as Super-Resolution Variable-Pixel Linear Reconstruction. The algorithm works combining different lower resolution images in order to obtain, as a result, a higher ...
Núñez de Murga, Jorge, 1955- +1 more
core +1 more source
Algorithms, 2019 Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem.
Yunshan Sun +3 more
doaj +1 more source
Guided filter-based multi-scale super-resolution reconstruction
CAAI Transactions on Intelligence Technology, 2020 The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through the network.
Xiaomei Feng +3 more
doaj +1 more source
Journal of Integrative Neuroscience, 2021 To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative ...
Chuluunbaatar Otgonbaatar +5 more
doaj +1 more source
Inexact Bregman iteration with an application to Poisson data reconstruction [PDF]
, 2013 This work deals with the solution of image restoration problems by an iterative regularization method based on the Bregman iteration. Any iteration of this scheme requires to exactly compute the minimizer of a function.
Ruggiero, V +3 more
core +1 more source
Data in Brief, 2017 This article describes a quantitative evaluation of visualizing small vessels using several image reconstruction methods in computed tomography. Simulated vessels with diameters of 1–6 mm made by 3D printer was scanned using 320-row detector computed ...
Toru Higaki +7 more
doaj +1 more source
Semi-blind sparse image reconstruction with application to MRFM [PDF]
, 2012 We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known.
Hero, Alfred O. +2 more
core +1 more source