MR Image Super Resolution by Combining Feature Disentanglement CNNs and Vision Transformers
Abstract
State of the art magnetic resonance (MR) image super-resolution methods (ISR) using convolutional neural networks (CNNs) leverage limited contextual information due to the limited spatial coverage of CNNs. Vision transformers (ViT) learn better global context that is helpful in generating superior quality HR images. We combine local information of CNNs and global information from ViTs for image super resolution and output super resolved images that have superior quality than those produced by state of the art methods. We include extra constraints through multiple novel loss functions that preserve structure and texture information from the low resolution to high resolution images.
Cite
Text
Mahapatra and Ge. "MR Image Super Resolution by Combining Feature Disentanglement CNNs and Vision Transformers." Medical Imaging with Deep Learning, 2023.Markdown
[Mahapatra and Ge. "MR Image Super Resolution by Combining Feature Disentanglement CNNs and Vision Transformers." Medical Imaging with Deep Learning, 2023.](https://mlanthology.org/midl/2023/mahapatra2023midl-mr/)BibTeX
@inproceedings{mahapatra2023midl-mr,
title = {{MR Image Super Resolution by Combining Feature Disentanglement CNNs and Vision Transformers}},
author = {Mahapatra, Dwarikanath and Ge, Zongyuan},
booktitle = {Medical Imaging with Deep Learning},
year = {2023},
pages = {858-878},
volume = {172},
url = {https://mlanthology.org/midl/2023/mahapatra2023midl-mr/}
}