ArticleNature biotechnology2020
Rapid image deconvolution and multiview fusion for optical microscopy.
Article in Nature biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed, 193 citations in OpenAlex.
- Efficient spatio-angular reconstruction enables high-fidelity mapping of six-dimensional structures and dynamics with polarized fluorescence microscopy.Nature communications · 2026Article
- A physics-informed alternative to Richardson-Lucy deconvolution across SNR regimes without iteration cutoffs.Nature communications · 2026Article
- Super-Resolution Structured Illumination Microscopy to Study Endocytosis and Cellular Nanoparticle Uptake.ACS nano · 2026Review
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- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
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- Patch deconvolution for Fourier light-field microscopy.Biophysical journal · 2026Article
- Improved 3D image reconstruction via deep-learning-based fusion of light-field microscopy and Fourier light-field microscopy images.Journal of biomedical optics · 2026Article
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- Point spread function decoupling in computational fluorescence microscopy.Light, science & applications · 2026Article
- Volumetric localization microscopy with deep learning.Nature communications · 2025Article
- Myelinated fiber labeling and orientation mapping of the human brain with light-sheet fluorescence microscopy.NeuroImage · 2025Article
- Depth-variant deconvolution applied to widefield microscopy for rapid large-volume tissue imaging.Communications biology · 2025Article
- Altair-LSFM A High-Resolution, Easy-to-Build Light-Sheet Microscope for Sub-Cellular Imaging.bioRxiv : the preprint server for biology · 2025Article
- Real-time, high-throughput super-resolution microscopy via panoramic integration.Nature communications · 2025Article
- Observing biological spatio-angular structures and dynamics with statistical image reconstruction and polarized fluorescence microscopy.bioRxiv : the preprint server for biology · 2025Article
- Super-resolution imaging with nanopipettes.Npj imaging · 2025Article
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- Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging.Nature methods · 2025Article
- Cancellation of Spectral and Spatial Crosstalk in Spectral Imaging for High-Dynamic-Range Electrophoretic Analysis of STR-PCR Products.Analytical chemistry · 2025Article
27 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
29 authors at 11 institutions in 3 countries.
Funding
Abstract
The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an 'unmatched back projector' accelerates deconvolution relative to the classic Richardson-Lucy algorithm by at least tenfold. Second, three-dimensional image-based registration with a graphics processing unit enhances processing speed 10- to 100-fold over CPU processing. Third, deep learning can provide further acceleration, particularly for deconvolution with spatially varying point spread functions. We illustrate our methods from the subcellular to millimeter spatial scale on diverse samples, including single cells, embryos and cleared tissue. Finally, we show performance enhancement on recently developed microscopes that have improved spatial resolution, including dual-view cleared-tissue light-sheet microscopes and reflective lattice light-sheet microscopes.
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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.