Improved Super Resolution of MR Images Using CNNs and Vision Transformers

Dwarikanath Mahapatra
7/24/2022
eess.IVcs.CV

No confident code implementation found for this paper. 5 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{mahapatra2022improved,
  title  = {Improved Super Resolution of MR Images Using CNNs and Vision Transformers},
  author = {Dwarikanath Mahapatra},
  year   = {2022},
  eprint = {2207.11748},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.11748v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2207.11748.svg)](https://codeofpaper.com/paper/2207.11748)

Discussion