ArticleLight, science & applications2022
Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams.
Article in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Bessel beam side lobe suppression via non-degenerate two-photon excitation.Biomedical optics express · 2026Article
- Imaging 3D cell cultures with optical microscopy.Nature methods · 2025Review
- Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating.Light, science & applications · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Super resolution reconstruction of fluorescence microscopy images by a convolutional network with physical priors.Biomedical optics express · 2024Article
- Sidelobe suppressed Bessel beams for one-photon light-sheet microscopy.Biomedical optics express · 2024Article
- Frontiers in artificial intelligence-directed light-sheet microscopy for uncovering biological phenomena and multi-organ imaging.View (Beijing, China) · 2024Article
- 3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing.BMC biology · 2024Article
- Evaluating the resolution of conventional optical microscopes through point spread function measurement.iScience · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.