Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams.

ABSTRACT: Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserve perceptual content. Our method rapidly and robustly deconvolves and super-resolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often requir

SUBMITTER: Wijesinghe P 

PROVIDER: S-EPMC9626625 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature