• Corpus ID: 220347676

Ground Truth Free Denoising by Optimal Transport

@article{Dittmer2020GroundTF,
  title={Ground Truth Free Denoising by Optimal Transport},
  author={S{\"o}ren Dittmer and Carola-Bibiane Sch{\"o}nlieb and Peter Maass},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.01575}
}
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals. The training is solely based on samples of noisy data and examples of noise, which -- critically -- do not need to come in pairs. We only need the assumption that the noise is independent and additive (although we describe how this can be extended). The method rests on a Wasserstein Generative Adversarial Network setting, which utilizes two critics and one… 
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