  We propose a novel self-supervised image blind denoising approach in which
two neural networks jointly predict the clean signal and infer the noise
distribution. Assuming that the noisy observations are independent
conditionally to the signal, the networks can be jointly trained without clean
training data. Therefore, our approach is particularly relevant for biomedical
image denoising where the noise is difficult to model precisely and clean
training data are usually unavailable. Our method significantly outperforms
current state-of-the-art self-supervised blind denoising algorithms, on six
publicly available biomedical image datasets. We also show empirically with
synthetic noisy data that our model captures the noise distribution
efficiently. Finally, the described framework is simple, lightweight and
computationally efficient, making it useful in practical cases.
