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State of the art magnetic resonance (MR) image super-resolution methods (ISR) leverage limited contextual information due to the use of CNNs, which learn interactions over a small neighborhood. On the other hand Vision transformers (ViT) have the ability to learn much more global contextual information, which is especially relevant for MR ISR since they provide additional information to generate superior quality HR images. We propose to 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. Additionally, we incorporate extra constraints through multiple novel loss functions that preserve structure and texture information from the low resolution to high resolution images.
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Thursday 7th July
Poster Session 2.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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