Adjacent slices feature transformer network for single anisotropic 3D brain MRI image super-resolution

2022 ◽  
Vol 72 ◽  
pp. 103339
Author(s):  
Lulu Wang ◽  
Huazheng Zhu ◽  
Zhongshi He ◽  
Yuanyuan Jia ◽  
Jinglong Du
Keyword(s):  
Brain Mri ◽  
Super Resolution ◽  
Image Super Resolution ◽  
Mri Image

scholarly journals Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57856-57867 ◽  
Author(s):  
Hong Zheng ◽  
Kun Zeng ◽  
Di Guo ◽  
Jiaxi Ying ◽  
Yu Yang ◽  
...  
Keyword(s):  
Brain Mri ◽  
Super Resolution ◽  
Edge Enhancement ◽  
Image Super Resolution ◽  
Mri Image

3D MRI image super-resolution for brain combining rigid and large diffeomorphic registration

2017 ◽  
Vol 11 (12) ◽  
pp. 1291-1301 ◽  
Author(s):  
Zifei Liang ◽  
Xiaohai He ◽  
Qizhi Teng ◽  
Dan Wu ◽  
Lingbo Qing
Keyword(s):  
Super Resolution ◽  
3D Mri ◽  
Diffeomorphic Registration ◽  
Image Super Resolution ◽  
Mri Image

scholarly journals Lorentzian Norm based Super-resolution Reconstruction of Brain MRI Image

2017 ◽  
Vol 4 (16) ◽  
pp. 153485
Author(s):  
Dongxing Bao ◽  
Xiaoming Li ◽  
Jin Li
Keyword(s):  
Brain Mri ◽  
Super Resolution ◽  
Mri Image

scholarly journals Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture

2020 ◽  
Vol 32 ◽  
pp. 03044
Author(s):  
Vanita Mane ◽  
Suchit Jadhav ◽  
Praneya Lal
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Mri ◽  
Super Resolution ◽  
Structural Mri ◽  
Reconstruction Performance ◽  
Human Connectome Project ◽  
Image Super Resolution

Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The 3D neural network generates output brain images of high-resolution (HR) from a low-resolution (LR) input image. A simple design ensures less time complexity and high reconstruction quality. The network is trained using T1-weighted structural MRI images from the human connectome project dataset which is a large publicly available brain MRI database.

scholarly journals FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution

2021 ◽  
pp. 101969
Author(s):  
Mingfeng Jiang ◽  
Minghao Zhi ◽  
Liying Wei ◽  
Xiaocheng Yang ◽  
Jucheng Zhang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Super Resolution ◽  
Mri Image

A Short Survey of Image Super Resolution Algorithms

2015 ◽  
Vol 2 (2) ◽  
pp. 19-29 ◽  
Author(s):  
Heng Liu ◽  
◽  
Dongdong Huang
Keyword(s):  
Super Resolution ◽  
Short Survey ◽  
Image Super Resolution

Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components

2013 ◽  
Vol E96.A (6) ◽  
pp. 1315-1322 ◽  
Author(s):  
Hyunduk KIM ◽  
Sang-Heon LEE ◽  
Myoung-Kyu SOHN ◽  
Dong-Ju KIM ◽  
Byungmin KIM
Keyword(s):  
Super Resolution ◽  
Facial Image ◽  
Frequency Components ◽  
Image Super Resolution

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution
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