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Shi, F., Cheng, J., Wang, L., Yap, P.T. and Shen, D. (2013) Low-Rank Total Variation for Image Super-Resolution. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C. and Navab, N., Eds., International Conference on Medical Image Computing and Computer-Assisted Intervention, Springe, Berlin, 155-162.
https://doi.org/10.1007/978-3-642-40811-3_20

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.