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

Philip Wijesinghe, Stella Corsetti, Darren J. X. Chow, Shuzo Sakata, Kylie R. Dunning, Kishan Dholakia

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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 require 1000–10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes, preimplantation embryos and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.
Original languageEnglish
Article number319
Number of pages15
JournalLight: Science & Applications
Volume11
Issue number1
DOIs
Publication statusPublished - 2 Nov 2022

Keywords

  • deconvolution
  • light-sheet microscopy
  • propagation-invariant beams

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