ArticleOptics express2020
Practical sensorless aberration estimation for 3D microscopy with deep learning.
Article in Optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Graph-based modeling of optical system enables adaptive optics on dynamic samples with self-calibration.iScience · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields.Nature methods · 2026Article
- Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens.Nature methods · 2025Article
- Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression.Nature methods · 2025Article
- Fourier-Based 3D Multistage Transformer for Aberration Correction in Multicellular Specimens.ArXiv · 2025Article
- Simultaneous particle tracking, phase retrieval and point spread function reconstruction.bioRxiv : the preprint server for biology · 2025Article
- Wavefront estimation through structured detection in laser scanning microscopy.Biomedical optics express · 2025Article
- Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.Nature communications · 2025Article
- Fringe Texture Driven Droplet Measurement End-to-End Network Based on Physics Aberrations Restoration of Coherence Scanning Interferometry.Micromachines · 2024Article
- Practical guidelines for cell segmentation models under optical aberrations in microscopy.Computational and structural biotechnology journal · 2024Article
- X-ray lens figure errors retrieved by deep learning from several beam intensity images.Journal of synchrotron radiation · 2024Article
- Live-cell imaging powered by computation.Nature reviews. Molecular cell biology · 2024Review
- Deep-blur: Blind identification and deblurring with convolutional neural networks.Biological imaging · 2024Article
- Universal adaptive optics for microscopy through embedded neural network control.Light, science & applications · 2023Article
- Adaptive optics for optical microscopy [Invited].Biomedical optics express · 2023Review
- Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope.Sensors (Basel, Switzerland) · 2022Article
- Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey.Frontiers in bioinformatics · 2022Review
- Adaptive optics in super-resolution microscopy.Biophysics reports · 2021Article
- Blind Deconvolution Based on Compressed Sensing with bi-International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Estimation of optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for 3D microscopy. Recent approaches based on deep learning promise accurate results at fast processing speeds. However, collecting ground truth microscopy data for training the network is typically very difficult or even impossible thereby limiting this approach in practice. Here, we demonstrate that neural networks trained only on simulated data yield accurate predictions for real experimental images. We validate our approach on simulated and experimental datasets acquired with two different microscopy modalities and also compare the results to non-learned methods. Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role. Finally, we make our implementation freely available as open source software in Python.
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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.