Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration.

ABSTRACT: Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging. Recently, deep-learning-based image deconvolution approaches have shown promise for image recovery. However, PAT imaging presents unique challenges, including spatially varying resolution and the absence of ground truth data. Consequently, there is a pressing need for a novel learning strategy specifically tailored for PAT imaging. Herein, we propose a configurable network model named Deep hybrid Image-PSF Prior (DIPP) that

SUBMITTER: Tang K 

PROVIDER: S-EPMC10413197 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature