The sparse-representation-based super resolution is an efficient learning-based method. This method involves two key steps. One is to learn two dictionaries for low/high-resolution image patches, and the other is to learn a mapping between low resolution example patches and their corresponding high resolution patches from massive external images. We presented a super resolution method for MRI from reduced k-space acquisition sequences via deep convolutional neural networks. The proposed method directly learns an end-to-end mapping between the low/high-resolution images.Our proposed method is tested on the OpenfMRI database. It significantly outperforms the zero-filled reconstruction and an existing learning-based MRI SR method.
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