ArticleNature communications2020
Fast and accurate sCMOS noise correction for fluorescence microscopy.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed, 163 citations in OpenAlex.
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- Real-time self-supervised denoising for high-speed fluorescence neural imaging.Nature communications · 2025Article
- Label-free robotic mitochondrial biopsy.Science advances · 2025Article
- Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting.Biophysical journal · 2025Article
- Bayesian Nonparametrics for FRET using Realistic Integrative Detectors.bioRxiv : the preprint server for biology · 2025Article
- Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting.bioRxiv : the preprint server for biology · 2025Article
- Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy.Nature methods · 2025Article
- Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy for rapid, label-free biological imaging.The Review of scientific instruments · 2025Article
- Advancing Platelet Research Through Live-Cell Imaging: Challenges, Techniques, and Insights.Sensors (Basel, Switzerland) · 2025Review
- Approaching maximum resolution in structured illumination microscopy via accurate noise modeling.Npj imaging · 2025Article
- Article
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- Reorganization of the flagellum scaffolding induces a sperm standstill during fertilization.eLife · 2024Article
- Self-inspired learning for denoising live-cell super-resolution microscopy.Nature methods · 2024Article
- A practical guide to light-sheet microscopy for nanoscale imaging: Looking beyond the cell.Journal of applied physics · 2024Article
5 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The rapid development of scientific CMOS (sCMOS) technology has greatly advanced optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view, and frame rates. However, for sCMOS sensors, the parallel charge-voltage conversion and different responsivity at each pixel induces extra readout and pattern noise compared to charge-coupled devices (CCD) and electron-multiplying CCD (EM-CCD) sensors. This can produce artifacts, deteriorate imaging capability, and hinder quantification of fluorescent signals, thereby compromising strategies to reduce photo-damage to live samples. Here, we propose a content-adaptive algorithm for the automatic correction of sCMOS-related noise (ACsN) for fluorescence microscopy. ACsN combines camera physics and layered sparse filtering to significantly reduce the most relevant noise sources in a sCMOS sensor while preserving the fine details of the signal. The method improves the camera performance, enabling fast, low-light and quantitative optical microscopy with video-rate denoising for a broad range of imaging conditions and modalities.
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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.