Improved Super Resolution of MR Images Using CNNs and Vision Transformers
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 64 of its 185 references that are in the map. First posted on arXiv 24 Jul 2022.
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64 of 185 references.
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