On-demand Learning for Deep Image Restoration

@article{Gao2017OndemandLF,
  title={On-demand Learning for Deep Image Restoration},
  author={Ruohan Gao and Kristen Grauman},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1095-1104}
}
  • Ruohan Gao, K. Grauman
  • Published 5 December 2016
  • Computer Science
  • 2017 IEEE International Conference on Computer Vision (ICCV)
While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty—such as a certain level of noise or blur. First, we examine the weakness of conventional “fixated” models and demonstrate that training general models to handle arbitrary levels of corruption is indeed non-trivial. Then, we propose an on-demand learning algorithm for training image restoration… 
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53 Citations
Highly Influential Citations
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Background Citations
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Results Citations
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53 Citations

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