Fully Unsupervised Probabilistic Noise2Void
This addresses the challenge of high-quality denoising for biomedical imaging without requiring extra calibration, though it is incremental over prior methods.
The paper tackles the problem of fully unsupervised image denoising in biomedical analysis by improving Probabilistic Noise2Void to eliminate the need for calibration data, achieving competitive results without additional data acquisition.
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.