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KRULL, A. VIČAR, T. PRAKASH, M. LALIT, M. JUG, F.
Original Title
Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
Type
journal article in Web of Science
Language
English
Original Abstract
Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods, such as Noise2Void (N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present Probabilistic Noise2Void (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a complete probabilistic model for the noisy observations and true signal in every pixel. We evaluate PN2V on publicly available microscopy datasets, under a broad range of noise regimes, and achieve competitive results with respect to supervised state-of-the-art methods.
Keywords
denoising, CARE, deep learning, microscopy data, probabilistic
Authors
KRULL, A.; VIČAR, T.; PRAKASH, M.; LALIT, M.; JUG, F.
Released
19. 2. 2020
Publisher
Frontiers Media SA
ISBN
2624-9898
Periodical
Frontiers in Computer Science
Year of study
2
Number
5
State
Swiss Confederation
Pages from
1
Pages to
9
Pages count
URL
https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full
Full text in the Digital Library
http://hdl.handle.net/11012/193231
BibTex
@article{BUT159778, author="Alexander {Krull} and Tomáš {Vičar} and Mangal {Prakash} and Manan {Lalit} and Florian {Jug}", title="Probabilistic Noise2Void: Unsupervised Content-Aware Denoising", journal="Frontiers in Computer Science", year="2020", volume="2", number="5", pages="1--9", doi="10.3389/fcomp.2020.00005", issn="2624-9898", url="https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full" }