Super-Resolution Using Enhanced U-Net for Brain MRI Images

Super-resolution is an important technique in image processing. It overcomes some hardware limitations failing to get the high-resolution images. After machine learning gets involved, the super-resolution technique gets more efficient in improving image quality. In this work, we applied super-resolution to the brain MRI images by proposing an enhanced U-Net. Firstly, we used U-Net to realize super-resolution on brain Magnetic Resonance Images (MRI). Secondly, we expanded the functionality of U-Net to the MRI with different contrasts by edge-to-edge training. Finally, we adopted transfer learning and employed the convolutional kernel loss function to improve the performance of the U-Net. Experimental results have shown the superiority of the proposed method, e.g., the resolution on rate was boosted from 81.49% by U-Net to 94.22% by our edge-to-edge training.

Super-resolution is an important technique in image processing. It overcomes some hardware limitations failing to get the high-resolution images. After machine learning gets involved, the super-resolution technique gets more efficient in improving image quality. In this work, we applied super-resolution to the brain MRI images by proposing an enhanced U-Net. Firstly, we used U-Net to realize super-resolution on brain Magnetic Resonance Images (MRI). Secondly, we expanded the functionality of U-Net to the MRI with different contrasts by edge-to-edge training. Finally, we adopted transfer learning and employed the convolutional kernel loss function to improve the performance of the U-Net. Experimental results have shown the superiority of the proposed method, e.g., the resolution on rate was boosted from 81.49% by U-Net to 94.22% by our edge-to-edge training.