Corpus ID: 221534501

Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution

  title={Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution},
  author={Rao Muhammad Umer and C. Micheloni},
  • Rao Muhammad Umer, C. Micheloni
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • Recent deep learning based single image super-resolution (SISR) methods mostly train their models in a clean data domain where the low-resolution (LR) and the high-resolution (HR) images come from noise-free settings (same domain) due to the bicubic down-sampling assumption. However, such degradation process is not available in real-world settings. We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions, which is inspired by the… CONTINUE READING
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