Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution-Reference-Cited by-同舟云学术

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Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Li Guangyuan¹,Lv Jun¹,Tian Yapeng²,Dou Qi³,Wang Chengyan⁴,Xu Chenliang¹,Qin Jing⁵

Affiliation:

1. School of Computer and Control Engineering, Yantai University,Yantai,China

2. University of Rochester

3. The Chinese University of Hong Kong,Department of Computer Science and Engineering,Hong Kong

4. Human Phenome Institute, Fudan University,Shanghai,China

5. Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University,Hong Kong

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879023.pdf?arnumber=9879023

Reference48 articles.

1. End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution

2. Deep-Learning-Based Multi-Modal Fusion for Fast MR Reconstruction

3. An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

4. High‐Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network

5. Multi-Modal Super-Resolution with Deep Guided Filtering

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2. Disentangled feature fusion network for lightweight image super-resolution;Digital Signal Processing;2024-11

3. Cross-contrast mutual fusion network for joint MRI reconstruction and super-resolution;Pattern Recognition;2024-10

4. Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution;IEEE Transactions on Neural Networks and Learning Systems;2024-09

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