Cross-modality deep learning brings bright-field microscopy contrast to holography
arXiv:1811.07103 · doi:10.1038/s41377-019-0139-9
Abstract
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of bright-field incoherent microscopy.
arXiv comment3 pages
References in corpus (3)
Cited by in corpus (33)
- Deep Learning-enabled Virtual Histological Staining of Biological Samples
- On the use of deep learning for phase recovery
- Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
- Terahertz Pulse Shaping Using Diffractive Surfaces
- Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light
- Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue
- Misalignment Resilient Diffractive Optical Networks
- Quantitative Digital Microscopy with Deep Learning
- Deep learning-based super-resolution in coherent imaging systems
- Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
- HoloStain: Holographic virtual staining of individual biological cells
- Self-supervised learning of hologram reconstruction using physics consistency
- Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
- Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning
- Scale-, shift- and rotation-invariant diffractive optical networks
- Deep learning-based holographic polarization microscopy
- Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
- Deep learning-based color holographic microscopy
- Adaptive 3D descattering with a dynamic synthesis network
- Extracting quantitative biological information from brightfield cell images using deep learning
- Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
- Physics-driven universal twin-image removal network for digital in-line holographic microscopy
- Deep learning phase recovery: data-driven, physics-driven, or combining both?
- Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
- Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
- Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network
- eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
- Calibration-free quantitative phase imaging using data-driven aberration modeling
- EM based Framework for Single-shot Compressive Holography
- Metasurfaces for infrared multi-modal microscopy: phase contrast and bright field
- Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram
- MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models
- Computer-free, all-optical reconstruction of holograms using diffractive networks