Shi, F., Cheng, J., Wang, L., Yap, P.T. and Shen, D. (2015) LRTV: MR Image Super-Resolution with Low-Rank and Total Variation Regularizations. IEEE Transactions on Medical Imaging, 34, 2459-2466.
https://doi.org/10.1109/TMI.2015.2437894

has been cited by the following article:

• TITLE: Super-Resolution Using Enhanced U-Net for Brain MRI Images

  AUTHORS: Dalei Jiang, Zifei Han, Xiaohan Zhu, Yang Zhou, Han Yang

  KEYWORDS: Image Super-Resolution, Machine Learning, Transfer Learning, Convolutional Kernel

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.11, November 30, 2022

  ABSTRACT: Super-resolution is an important technique in image processing. It overcomes some hardware limitations failing to get high-resolution image. After machine learning gets involved, the super-resolution technique gets more efficient in improving the 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 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.