Generalised Probabilistic Diffusion Scale-Spaces
Pascal Peter
Probabilistic diffusion models excel at sampling new images from learned
distributions. Originally motivated by drift-diffusion concepts from physics,
they apply image perturbations such as noise and blur in a forward process that
results in a tractable probability distribution. A corresponding learned
reverse process generates images and can be conditioned on side information,
which leads to a wide variety of practical applications. Most of the research
focus currently lies on practice-oriented extensions. In contrast, the
theoretical background remains largely unexplored, in particular the relations
to drift-diffusion. In order to shed light on these connections to classical
image filtering, we propose a generalised scale-space theory for probabilistic
diffusion models. Moreover, we show conceptual and empirical connections to
diffusion and osmosis filters.