Ground Truth Free Denoising by Optimal Transport

• URL: http://arxiv.org/abs/2007.01575v1
• Date: Fri, 3 Jul 2020 09:39:25 GMT
• Title: Ground Truth Free Denoising by Optimal Transport
• Authors: S\"oren Dittmer, Carola-Bibiane Sch\"onlieb, Peter Maass
• Abstract summary: We present a learned unsupervised denoising method for arbitrary types of data. The training is solely based on samples of noisy data and examples of noise. The method rests on a Wasserstein Generative Adversarial Network setting.
• Score: 2.5137859989323537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 generator.

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