Paper Info
Title
Wide Weighted Attention Multi-Scale Network for Accurate MR Image Super-Resolution
Abstract
High-quality magnetic resonance (MR) images afford more detailed information for reliable diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep convolutional neural network (CNN) has shown its promising ability for image super-resolution (SR). The LR MR images usually share some visual characteristics: structural textures of different sizes, edges with high correlat...
Year
DOI
Venue
2022
10.1109/TCSVT.2021.3070489
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
DocType
Volume
Feature extraction,Convolution,Superresolution,Task analysis,Image reconstruction,Medical diagnostic imaging,Deep learning
Journal
32
Issue
ISSN
Citations 
3
1051-8215
2
PageRank 
References 
Authors
0.37
0
4
Name
Order
Citations
PageRank
Wang H17129.35
Xiaowan Hu220.37
Xiaole Zhao3142.38
Zhang Yulun420622.15