Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU134052" target="_blank" >RIV/00216305:26220/20:PU134052 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fcomp.2020.00005" target="_blank" >10.3389/fcomp.2020.00005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Computer Science

ISSN

2624-9898

e-ISSN

—

Svazek periodika

2

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

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EID výsledku v databázi Scopus

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