Dictionary-based super resolution for positron emission tomography images
In this paper, a strategy to increase the resolution of positron emission tomography (PET) images, using a previously trained high resolution dictionary for the sinograms is proposed. The low resolution input sinogram is divided into patches of 5x5 samples. The sparse code of each patch is calc...
Uloženo v:
Hlavní autor: | Rodríguez Hernández, Leandro José |
---|---|
Další autoři: | Ochoa Domínguez, Humberto |
Médium: | Artículo |
Jazyk: | en_US |
Vydáno: |
2019
|
Témata: | |
On-line přístup: | https://ijcopi.org/index.php/ojs/article/view/151 |
Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo otaguje tento záznam!
|
Podobné jednotky
-
Residual 3D convolutional neural network to enhance sinograms from small-animal positron emission tomography images
Vydáno: (2023) -
Reconstruction of PET Images Using Cross-Entropy and Field of Experts
Autor: Mejia, Jose
Vydáno: (2019) -
Overview of Super-resolution Techniques
Autor: Morera Delfín, Leandro
Vydáno: (2018) -
Auto-regularized Gradients of Adaptive Interpolation for MRI Super-Resolution
Autor: Morera Delfin, Leandro
Vydáno: (2018) -
Deep learning-based super resolution methodology for positron emission tomography imaging: 4CP22-29
Autor: Leandro Rodríguez Hernández, a další
Vydáno: (2022)