ArticleNature communications2022
Photon-free (s)CMOS camera characterization for artifact reduction in high- and super-resolution microscopy.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Characterisation of the signal to noise ratio of 2-photon microscopes.Scientific reports · 2026Article
- Preparation and Performance Optimization of High-Strength, Tough, and Conductive Poly(vinyl alcohol)/Polyacrylamide/Polyaniline Hydrogels.ACS omega · 2026Article
- Simulating Multicolor Super-Resolution Imaging Using an RGB Camera.Computational and structural biotechnology journal · 2026Article
- Approaching maximum resolution in structured illumination microscopy via accurate noise modeling.Npj imaging · 2025Article
- Three-dimensional localization and tracking of chromosomal loci throughout the Escherichia coli cell cycle.Communications biology · 2024Article
- Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution.Sensors (Basel, Switzerland) · 2024Article
- Build and operation of a custom 3D, multicolor, single-molecule localization microscope.Nature protocols · 2024Review
- Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy.Nature communications · 2024Article
- Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images.Science advances · 2023Article
- Single-molecule tracking (SMT): a window into live-cell transcription biochemistry.Biochemical Society transactions · 2023Article
- Article
- Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy.Frontiers in bioinformatics · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Modern implementations of widefield fluorescence microscopy often rely on sCMOS cameras, but this camera architecture inherently features pixel-to-pixel variations. Such variations lead to image artifacts and render quantitative image interpretation difficult. Although a variety of algorithmic corrections exists, they require a thorough characterization of the camera, which typically is not easy to access or perform. Here, we developed a fully automated pipeline for camera characterization based solely on thermally generated signal, and implemented it in the popular open-source software Micro-Manager and ImageJ/Fiji. Besides supplying the conventional camera maps of noise, offset and gain, our pipeline also gives access to dark current and thermal noise as functions of the exposure time. This allowed us to avoid structural bias in single-molecule localization microscopy (SMLM), which without correction is substantial even for scientific-grade, cooled cameras. In addition, our approach enables high-quality 3D super-resolution as well as live-cell time-lapse microscopy with cheap, industry-grade cameras. As our approach for camera characterization does not require any user interventions or additional hardware implementations, numerous correction algorithms that rely on camera characterization become easily applicable.
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Registered trials
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