论文标题
使用CNN和视觉变压器改善了MR图像的超级分辨率
Improved Super Resolution of MR Images Using CNNs and Vision Transformers
论文作者
论文摘要
使用卷积神经网络（CNN）的最先进的磁共振（MR）图像超分辨率方法（ISR）由于CNN的空间覆盖率有限，因此利用有限的上下文信息。 Vision Transformers（VIT）学习更好的全球环境，这有助于产生卓越的质量人力资源图像。我们将CNN的本地信息和来自VIT的全局信息结合在一起，用于图像超级分辨率和输出超级分辨率的图像，这些图像比最先进的方法具有优越的质量。我们通过多种新颖的损失函数包括额外的约束，这些损失功能将结构和纹理信息从低分辨率到高分辨率图像。
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.
