Traditional inception-based convolutional neural networks (CNN) are proved to be capable of tackling high resolution image restoration, yet they are poor at generalization due to the supervised learning procedure. We proposed a combination of CNN-based super resolution network and generative adversarial network, to make full use of the learning of high resolution from CNN, as well as to improve the generalization of the network, by preserving the original contrast of the sequence. The result shows that our proposed network could perform MR super resolution across sequences with higher quality than that from a single CNN network.
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