ArticleNature methods2022
Incorporating the image formation process into deep learning improves network performance.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 110 citations in OpenAlex.
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- Deep-Learning-Driven High-Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon-Limited Conditions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From stars to molecules: AI guided device-agnostic super-resolution imaging.Nature communications · 2026Article
- A deep learning-enabled toolkit for the 3D segmentation of ventricular cardiomyocytes.The Journal of physiology · 2026Article
- A physics-informed alternative to Richardson-Lucy deconvolution across SNR regimes without iteration cutoffs.Nature communications · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
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- Super-Resolution Structured Illumination Microscopy to Study Endocytosis and Cellular Nanoparticle Uptake.ACS nano · 2026Review
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- Revisiting PSF models: Unifying framework and high-performance implementation.Journal of microscopy · 2026Article
- High-speed blind structured illumination microscopy via unsupervised algorithm unrolling.Nature communications · 2026Article
- Enhancing volumetric microscopy with blind computational correction of spatially variant aberrations using neural field representation.bioRxiv : the preprint server for biology · 2026Article
- Point spread function decoupling in computational fluorescence microscopy.Light, science & applications · 2026Article
- A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification.Nature biotechnology · 2026Article
- Stimulated Raman Scattering Imaging Enabled Biomarker Discovery for Precision Medicine.Chemical & biomedical imaging · 2025Review
- Observing biological spatio-angular structures and dynamics with statistical image reconstruction and polarized fluorescence microscopy.bioRxiv : the preprint server for biology · 2025Article
- NeuroDecon: A Neural Network-Based Method for Three-Dimensional Deconvolution of Fluorescent Microscopic Images.International journal of molecular sciences · 2025Article
- Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy.Nature methods · 2025Article
Corrections and comments
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Authors and funding
18 authors at 7 institutions in 2 countries.
Funding
Abstract
We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN's performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.
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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.