ArticleApplied physics letters2019
Accurate phase retrieval of complex 3D point spread functions with deep residual neural networks.
Article in Applied physics letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- 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
- Joint estimation of point spread function and molecule positions in SMLM informed from multiple planes.Biomedical optics express · 2025Article
- X-ray lens figure errors retrieved by deep learning from several beam intensity images.Journal of synchrotron radiation · 2024Article
- Universal inverse modeling of point spread functions for SMLM localization and microscope characterization.Nature methods · 2024Article
- Using Diffraction Deep Neural Networks for Indirect Phase Recovery Based on Zernike Polynomials.Sensors (Basel, Switzerland) · 2024Article
- Deep-blur: Blind identification and deblurring with convolutional neural networks.Biological imaging · 2024Article
- Aberration Estimation for Synthetic Aperture Digital Holographic Microscope Using Deep Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Universal adaptive optics for microscopy through embedded neural network control.Light, science & applications · 2023Article
- Universal inverse modelling of point spread functions for SMLM localization and microscope characterization.bioRxiv : the preprint server for biology · 2023Article
- Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules.Optics communications · 2023Article
- Deep learning-based adaptive optics for light sheet fluorescence microscopy.Biomedical optics express · 2023Article
- Fast High-Resolution Phase Diversity Wavefront Sensing with L-BFGS Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Adaptive optics for optical microscopy [Invited].Biomedical optics express · 2023Review
- Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping.Communications biology · 2023Article
- Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks.Nature communications · 2022Article
- Deep learning in single-molecule imaging and analysis: recent advances and prospects.Chemical science · 2022Review
- Jitter-Robust Phase Retrieval Wavefront Sensing Algorithms.Sensors (Basel, Switzerland) · 2022Article
- Light Sheet Illumination for 3D Single-Molecule Super-Resolution Imaging of Neuronal Synapses.Frontiers in synaptic neuroscience · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Phase retrieval, i.e., the reconstruction of phase information from intensity information, is a central problem in many optical systems. Imaging the emission from a point source such as a single molecule is one example. Here, we demonstrate that a deep residual neural net is able to quickly and accurately extract the hidden phase for general point spread functions (PSFs) formed by Zernike-type phase modulations. Five slices of the 3D PSF at different focal positions within a two micrometer range around the focus are sufficient to retrieve the first six orders of Zernike coefficients.
Identifiers
What OpenQuestion holds
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.