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Single MR Image Super-Resolution using Generative Adversarial Network

arXiv 2207.08036 ↗First posted 16 Jul 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Spatial resolution of medical images can be improved using super-resolution methods. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images, given input images of lower resolution. In this paper, we apply this method to enhance the spatial resolution of 2D MR images. In our proposed approach, we slightly modify the structure of the Real-ESRGAN to train 2D Magnetic Resonance images (MRI) taken from the Brain Tumor Segmentation Challenge (BraTS) 2018 dataset. The obtained results are validated qualitatively and quantitatively by computing SSIM (Structural Similarity Index Measure), NRMSE (Normalized Root Mean Square Error), MAE (Mean Absolute Error), and VIF (Visual Information Fidelity) values.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 32 references that are in the map. First posted on arXiv 16 Jul 2022.

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References · 24

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12 more
  1. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  2. 2018The relativistic discriminator: a key element missing from standard GANcited by 234
  3. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  4. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
  5. 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
  6. 2018Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Networkcited by 64
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  8. 2018Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networkscited by 46
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  11. 2018Brain MRI super-resolution using 3D generative adversarial networkscited by 15
  12. 2020Arbitrary Scale Super-Resolution for Brain MRI Imagescited by 6

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