广义扩散概率尺度空间
Generalised Diffusion Probabilistic Scale-Spaces
AI总结：
本文提出扩散概率模型的广义尺度空间理论，揭示其与漂移-扩散及经典扩散、渗透滤波器的概念和实证联系，弥补理论背景研究不足。
摘要：
扩散概率模型擅长从学习到的分布中采样新图像。这类模型最初受物理学中漂移-扩散概念的启发，在前向过程中对图像施加噪声和模糊等扰动，从而得到一个可处理的概率分布。相应的学习型反向过程生成图像，并可以以辅助信息为条件，由此带来了广泛的实际应用。目前研究重点主要放在面向实践的扩展上。相比之下，其理论背景在很大程度上仍未得到探索，尤其是与漂移-扩散之间的关系。为了阐明这些与经典图像滤波之间的联系，我们提出了一种适用于扩散概率模型的广义尺度空间理论。此外，我们展示了其与扩散滤波器和渗透滤波器的概念性及实证性联系。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
