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- W4286409076 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." @default.
- W4286409076 created "2022-07-22" @default.
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- W4286409076 date "2020-07-03" @default.
- W4286409076 modified "2023-09-28" @default.
- W4286409076 title "Ground Truth Free Denoising by Optimal Transport" @default.
- W4286409076 doi "https://doi.org/10.48550/arxiv.2007.01575" @default.
- W4286409076 hasPublicationYear "2020" @default.
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