Improved Super Resolution of MR Images Using CNNs and Vision Transformers
This improves MR image quality for medical imaging applications, though it appears incremental as it hybridizes existing methods.
The paper tackles the problem of limited contextual information in MR image super-resolution by combining CNNs and Vision Transformers, resulting in superior quality high-resolution images compared to state-of-the-art methods.
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.