ArticleLight, science & applications2023
E2E-BPF microscope: extended depth-of-field microscopy using learning-based implementation of binary phase filter and image deconvolution.
Article in Light, science & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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.
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Who cites it
9 citing papers in PubMed.
- Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026Article
- A coma pattern-based autofocusing method resolves bacterial cold shock response at single-cell level.eLife · 2026Article
- Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction.Light, science & applications · 2026Article
- Digital defocus aberration interference for automated optical microscopy.Nature communications · 2026Article
- Multiplane 2.5D microscopy for high-throughput high-resolution tissue imaging.Journal of biomedical optics · 2025Article
- Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-per-second 3D imaging with deep learning.Light, science & applications · 2025Article
- Extended-depth of field random illumination microscopy, EDF-RIM, provides super-resolved projective imaging.Light, science & applications · 2024Article
- Highly sensitive volumetric single-molecule imaging.Nanophotonics (Berlin, Germany) · 2024Article
- Deep learning-enhanced microscopy with extended depth-of-field.Light, science & applications · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Several image-based biomedical diagnoses require high-resolution imaging capabilities at large spatial scales. However, conventional microscopes exhibit an inherent trade-off between depth-of-field (DoF) and spatial resolution, and thus require objects to be refocused at each lateral location, which is time consuming. Here, we present a computational imaging platform, termed E2E-BPF microscope, which enables large-area, high-resolution imaging of large-scale objects without serial refocusing. This method involves a physics-incorporated, deep-learned design of binary phase filter (BPF) and jointly optimized deconvolution neural network, which altogether produces high-resolution, high-contrast images over extended depth ranges. We demonstrate the method through numerical simulations and experiments with fluorescently labeled beads, cells and tissue section, and present high-resolution imaging capability over a 15.5-fold larger DoF than the conventional microscope. Our method provides highly effective and scalable strategy for DoF-extended optical imaging system, and is expected to find numerous applications in rapid image-based diagnosis, optical vision, and metrology.
Identifiers
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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.