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A Deep-Learning-Based Label-free No-Reference Image Quality Assessment
  Metric: Application in Sodium MRI Denoising
v1v2 (latest)

A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising

29 August 2024
Shuaiyu Yuan
Tristan Whitmarsh
Dimitri A. Kessler
O. Arponen
Mary A McLean
G. Baxter
Frank Riemer
Aneurin Kennerley
William J Brackenbury
Fiona J Gilbert
Joshua D Kaggie
ArXiv (abs)PDFHTML

Papers citing "A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising"

12 / 12 papers shown
Title
SwinIR: Image Restoration Using Swin Transformer
SwinIR: Image Restoration Using Swin Transformer
Christos Sakaridis
Jie Cao
Guolei Sun
Peng Sun
Luc Van Gool
Radu Timofte
ViT
193
2,945
0
23 Aug 2021
MUSIQ: Multi-scale Image Quality Transformer
MUSIQ: Multi-scale Image Quality Transformer
Junjie Ke
Qifei Wang
Yilin Wang
P. Milanfar
Feng Yang
223
679
0
12 Aug 2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at
  Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy
Lucas Beyer
Alexander Kolesnikov
Dirk Weissenborn
Xiaohua Zhai
...
Matthias Minderer
G. Heigold
Sylvain Gelly
Jakob Uszkoreit
N. Houlsby
ViT
667
41,369
0
22 Oct 2020
Image Quality Assessment: Unifying Structure and Texture Similarity
Image Quality Assessment: Unifying Structure and Texture Similarity
Keyan Ding
Kede Ma
Shiqi Wang
Eero P. Simoncelli
106
787
0
16 Apr 2020
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of
  Picture Quality
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Zhenqiang Ying
Haoran Niu
Praful Gupta
D. Mahajan
Deepti Ghadiyaram
A. Bovik
86
307
0
20 Dec 2019
KonIQ-10k: An ecologically valid database for deep learning of blind
  image quality assessment
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Vlad Hosu
Hanhe Lin
T. Szirányi
Dietmar Saupe
108
574
0
14 Oct 2019
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Y. Zhang
Phillip Isola
Alexei A. Efros
Eli Shechtman
Oliver Wang
EGVM
379
11,877
0
11 Jan 2018
Road Extraction by Deep Residual U-Net
Road Extraction by Deep Residual U-Net
Zhengxin Zhang
Qingjie Liu
Yunhong Wang
SSeg
80
2,134
0
29 Nov 2017
Deep Neural Networks for No-Reference and Full-Reference Image Quality
  Assessment
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
S. Bosse
Dominique Maniry
K. Müller
Thomas Wiegand
Wojciech Samek
80
998
0
06 Dec 2016
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Justin Johnson
Alexandre Alahi
Li Fei-Fei
SupR
237
10,262
0
27 Mar 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg3DV
1.9K
77,341
0
18 May 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,479
0
04 Sep 2014
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