Corpus ID: 210116732

Self-Supervised Fast Adaptation for Denoising via Meta-Learning

@article{Lee2020SelfSupervisedFA,
  title={Self-Supervised Fast Adaptation for Denoising via Meta-Learning},
  author={Seunghwan Lee and Donghyeon Cho and J. Kim and T. Kim},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.02899}
}

Seunghwan Lee, Donghyeon Cho, +1 author T. Kim

Published 2020

Computer Science

ArXiv

Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i.e., internal statistics) at test time. However, self-supervised methods are not yet combined with conventional supervised denoising methods which train the denoising networks with a large number of external training samples… CONTINUE READING

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