ArticleLight, science & applications2019
Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram.
Article in Light, science & applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 138 citations in OpenAlex.
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- TIE-GANs: single-shot quantitative phase imaging using transport of intensity equation with integration of GANs.Journal of biomedical optics · 2024Article
- Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023Article
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Virtual differential phase-contrast and dark-field imaging of x-ray absorption images via deep learning.Bioengineering & translational medicine · 2023Article
- Deep-learning-based cross-modality translation from Stokes image to bright-field contrast.Journal of biomedical optics · 2023Article
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- Classification of Holograms with 3D-CNN.Sensors (Basel, Switzerland) · 2022Article
- Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization.Light, science & applications · 2022Article
- Article
- Real-time 3D imaging of ocean algae with crosstalk suppressed single-shot digital holographic microscopy.Biomedical optics express · 2022Article
- Deep learning in light-matter interactions.Nanophotonics (Berlin, Germany) · 2022Review
- Deep learning-based image processing in optical microscopy.Biophysical reviews · 2022Review
- Comprehensive deep learning model for 3D color holography.Scientific reports · 2022Article
- A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2022Review
- Light People: Professor Aydogan Ozcan.Light, science & applications · 2021Article
- Extracting quantitative biological information from bright-field cell images using deep learning.Biophysics reviews · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram. However, unlike a conventional bright-field microscopy image, the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects. Here, we demonstrate that cross-modality deep learning using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast, combining the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of incoherent bright-field microscopy. We illustrate the performance of this "bright-field holography" method through the snapshot imaging of bioaerosols distributed in 3D, matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging, and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram, benefiting from the wave-propagation framework of holography.
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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.