Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

journal article in Web of Science

English

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.

denoising, CARE, deep learning, microscopy data, probabilistic

KRULL, A.; VIČAR, T.; PRAKASH, M.; LALIT, M.; JUG, F.

19. 2. 2020

Frontiers Media SA

2624-9898

Frontiers in Computer Science

2

5

Swiss Confederation

1

9

9

https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full

http://hdl.handle.net/11012/193231