Example-Based Super-Resolution Image Reconstruction for Positron Emission Tomography Using Sparse Coding

This paper presents example-based methods for super-resolution (SR) reconstruction from a single set of low-resolution projections (or a sinogram) in positron emission tomography (PET). While deep learning-based SR approaches have shown promise across various imaging modalities, their application in...

詳細記述

書誌詳細

主要な著者:	Xue Ren, Soo-Jin Lee
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2024-01-01
シリーズ:	IEEE Access
主題:	Image reconstruction super-resolution sparse coding penalized-likelihood methods inverse problems positron emission tomography
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10772437/
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!