Magnetic resonance image (MRI) super-resolution (SR) algorithms have been applied to increase the spatial resolution of scans after acquisition, thus facilitating the clinical diagnosis. Motivated by the great success of deep convolutional neural network in computer vision, we introduced an Enhanced Recursive Residual Network (ERRN) for MRI SR. We show that the performance of our method exceeds conventional learning based methods (sparse coding-based ScSR, CNN-based SRCNN and VDSR) in terms of reconstruction error, peak-signal-to-noise-ratio (PSNR) and structure similarity index (SSIM) value.
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