Blind Motion Deblurring with Cycle Generative Adversarial Networks

@article{Yuan2019BlindMD,
  title={Blind Motion Deblurring with Cycle Generative Adversarial Networks},
  author={Quan Yuan and Junxia Li and Lingwei Zhang and Zhefu Wu and Guangyu Liu},
  journal={CoRR},
  year={2019},
  volume={abs/1901.01641}
}
Blind motion deblurring is one of the most basic and challenging problems in image processing and computer vision. It aims to recover a sharp image from its blurred version knowing nothing about the blur process. Many existing methods use Maximum A Posteriori (MAP) or Expectation Maximization (EM) frameworks to deal with this kind of problems, but they cannot handle well the figh frequency features of natural images. Most recently, deep neural networks have been emerging as a powerful tool for… CONTINUE READING
9
Twitter Mentions

References

Publications referenced by this paper.
SHOWING 1-10 OF 50 REFERENCES

Physics-Based Generative Adversarial Models for Image Restoration and Beyond

Jinshan Pan, Yang P. Liu, +5 authors Ming-Hsuan Yang
  • ArXiv
  • 2018
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Least Squares Generative Adversarial Networks

  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • 2017
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Making a “Completely Blind” Image Quality Analyzer

  • IEEE Signal Processing Letters
  • 2013
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Sparse representation based blind image deblurring

  • 2011 IEEE International Conference on Multimedia and Expo
  • 2011
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Image information and visual quality

  • IEEE Trans. Image Processing
  • 2006
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers

Loading similar papers…