• Corpus ID: 220347676

Ground Truth Free Denoising by Optimal Transport

  title={Ground Truth Free Denoising by Optimal Transport},
  author={S{\"o}ren Dittmer and Carola-Bibiane Sch{\"o}nlieb and Peter Maass},
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… 
1 Citations
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    2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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