  Diffusion and Poisson flow models have shown impressive performance in a wide
range of generative tasks, including low-dose CT image denoising. However, one
limitation in general, and for clinical applications in particular, is slow
sampling. Due to their iterative nature, the number of function evaluations
(NFE) required is usually on the order of $10-10^3$, both for conditional and
unconditional generation. In this paper, we present posterior sampling Poisson
flow generative models (PPFM), a novel image denoising technique for low-dose
and photon-counting CT that produces excellent image quality whilst keeping
NFE=1. Updating the training and sampling processes of Poisson flow generative
models (PFGM)++, we learn a conditional generator which defines a trajectory
between the prior noise distribution and the posterior distribution of
interest. We additionally hijack and regularize the sampling process to achieve
NFE=1. Our results shed light on the benefits of the PFGM++ framework compared
to diffusion models. In addition, PPFM is shown to perform favorably compared
to current state-of-the-art diffusion-style models with NFE=1, consistency
models, as well as popular deep learning and non-deep learning-based image
denoising techniques, on clinical low-dose CT images and clinical images from a
prototype photon-counting CT system.

Image and Video Processing;Computer Vision and Pattern Recognition;Medical Physics