Echocardiographic Image Quality Assessment Using Deep Neural Networks

2021 ◽  
pp. 488-502
Author(s):  
Robert B. Labs ◽  
Massoud Zolgharni ◽  
Jonathan P. Loo
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Deep Neural Networks ◽  
Image Quality Assessment ◽  
Echocardiographic Image

No-reference image quality assessment with shearlet transform and deep neural networks

2015 ◽  
Vol 154 ◽  
pp. 94-109 ◽  
Author(s):  
Yuming Li ◽  
Lai-Man Po ◽  
Xuyuan Xu ◽  
Litong Feng ◽  
Fang Yuan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Deep Neural Networks ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Shearlet Transform

scholarly journals Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

2018 ◽  
Vol 27 (1) ◽  
pp. 206-219 ◽  
Author(s):  
Sebastian Bosse ◽  
Dominique Maniry ◽  
Klaus-Robert Muller ◽  
Thomas Wiegand ◽  
Wojciech Samek
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Deep Neural Networks ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Full Reference

End-to-End Blind Image Quality Assessment Using Deep Neural Networks

2018 ◽  
Vol 27 (3) ◽  
pp. 1202-1213 ◽  
Author(s):  
Kede Ma ◽  
Wentao Liu ◽  
Kai Zhang ◽  
Zhengfang Duanmu ◽  
Zhou Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Deep Neural Networks ◽  
Image Quality Assessment ◽  
Blind Image Quality Assessment ◽  
End To End ◽  
Blind Image

A Study on Image Quality Assessment using Neural Networks and Structure Similarty

2011 ◽  
Vol 6 (10) ◽  
Author(s):  
Chih-hsien Kung ◽  
Wei-sheng Yang ◽  
Chih-ming Kung
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment

CNN-based image quality assessment of consumer photographs

2020 ◽  
Vol 2020 (1) ◽  
pp. 129-133
Author(s):  
Luigi Celona ◽  
Raimondo Schettini
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Building Blocks ◽  
Training Data ◽  
Computer Vision Applications ◽  
Feasible Solutions ◽  
World Image

Convolutional neural networks (CNNs) are used in an increasingly systematic way in a great variety of computer vision applications, including image quality assessment. However, their application to evaluate the perceived quality of images is strongly limited by the lack of adequate and consistent training data. A CNN-based framework for evaluating image quality of consumer photographs is made up of several building blocks that can be implemented in different ways. In this article, we schematically illustrate how these building blocks have been implemented and combined so far to create feasible solutions that take the most positive characteristics of CNNs while mitigating their intrinsic limitations. Some experimental results are reported to show the effectiveness of CNN-based solutions on real-world image quality datasets.

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