  Image denoising is the first step in many biomedical image analysis pipelines
and Deep Learning (DL) based methods are currently best performing. A new
category of DL methods such as Noise2Void or Noise2Self can be used fully
unsupervised, requiring nothing but the noisy data. However, this comes at the
price of reduced reconstruction quality. The recently proposed Probabilistic
Noise2Void (PN2V) improves results, but requires an additional noise model for
which calibration data needs to be acquired. Here, we present improvements to
PN2V that (i) replace histogram based noise models by parametric noise models,
and (ii) show how suitable noise models can be created even in the absence of
calibration data. This is a major step since it actually renders PN2V fully
unsupervised. We demonstrate that all proposed improvements are not only
academic but indeed relevant.
