ArticleScientific reports2022
Deep learning for blind structured illumination microscopy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 28 citations in OpenAlex.
- Advanced Imaging for Live-Cell Spatiotemporal Monitoring: Technologies and Applications.Research (Washington, D.C.) · 2026Review
- Article
- Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data.GigaScience · 2024Article
- Stochastically structured illumination microscopy scan less super resolution imaging.Npj imaging · 2024Article
- Live-cell imaging in the deep learning era.Current opinion in cell biology · 2023Review
- Superresolution structured illumination microscopy reconstruction algorithms: a review.Light, science & applications · 2023Review
- Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
Abstract
Blind-structured illumination microscopy (blind-SIM) enhances the optical resolution without the requirement of nonlinear effects or pre-defined illumination patterns. It is thus advantageous in experimental conditions where toxicity or biological fluctuations are an issue. In this work, we introduce a custom convolutional neural network architecture for blind-SIM: BS-CNN. We show that BS-CNN outperforms other blind-SIM deconvolution algorithms providing a resolution improvement of 2.17 together with a very high Fidelity (artifacts reduction). Furthermore, BS-CNN proves to be robust in cross-database variability: it is trained on synthetically augmented open-source data and evaluated on experiments. This approach paves the way to the employment of CNN-based deconvolution in all scenarios in which a statistical model for the illumination is available while the specific realizations are unknown or noisy.
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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.