Generalised Scale-Space Properties for Probabilistic Diffusion Models

Peter, Pascal

doi:10.1007/978-3-031-31975-4_46

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-31975-4_46

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Generalised Scale-Space Properties for Probabilistic Diffusion Models

Pascal Peter

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2024

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Generalised Diffusion Probabilistic Scale-Spaces

Peter

2024

J Math Imaging Vis

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Diffusion probabilistic 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 diffusion probabilistic models. Moreover, we show conceptual and empirical connections to diffusion and osmosis filters.

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Generalised Diffusion Probabilistic Scale-Spaces

Peter

2024

J Math Imaging Vis

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Generalised Scale-Space Properties for Probabilistic Diffusion Models

Cited by 1 publication

References 19 publications

Generalised Diffusion Probabilistic Scale-Spaces

Generalised Diffusion Probabilistic Scale-Spaces

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