  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.
