期刊
FRONTIERS IN COMPUTER SCIENCE
卷 2, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fcomp.2020.00005
关键词
denoising; CARE; deep learning; microscopy data; probabilistic
资金
- Max Planck Institute for Molecular Cell Biology and Genetics
- Max Planck Institute for Physics of Complex Systems
- Brno University of Technology
- German Federal Ministry of Research and Education [01IS18026C]
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.
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