Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.

ABSTRACT: Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial n

SUBMITTER: Park H 

PROVIDER: S-EPMC9178036 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature