Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Fragmentation Analysis of the MYC Enhancer in Cell-Free DNA by qPCR for Early Detection of Hepatocellular Carcinoma.

Hepatology research : the official journal of the Japan Society of Hepatology·2026

Same author

Consistency versus variability: Contrasting microbial ecological strategies of anaerobic digestion and activated sludge systems in full-scale Baijiu distillery wastewater treatment plants.

Water research·2026

Same author

gSV: a general structural variant detector using the third-generation sequencing data.

Briefings in bioinformatics·2026

Same author

Spatially differentiated conductive materials enable synergistic optimization of methane production and ARGs suppression in two-phase anaerobic treatment of antibiotic wastewater.

Water research·2026

Same author

Trait-Based Optimization of Plant Density in Drip-Fertigated Wheat: Yield Formation and Nitrogen-Radiation-Water Use Efficiency Responses of Varieties Contrasting in Individual Spike Productivity.

Plants (Basel, Switzerland)·2026

Same author

LSTM-based prediction and early warning of nitrogen removal in a partial denitrification-anammox bioreactor for municipal wastewater.

Journal of environmental management·2026

Same journal

Development of a fast-crosslinking hydrogel system doped with magnetic mesoporous nanoparticles for sustained fluoride ion release and caries prevention.

Frontiers in bioengineering and biotechnology·2026

Same journal

Editorial: Advancements in research on plant-derived extracellular vesicles and nanoparticles- applications in biotechnology and one health.

Frontiers in bioengineering and biotechnology·2026

Same journal

Operational integrity screening for telemedicine workflows: an explainable motion and audiovisual coherence framework.

Frontiers in bioengineering and biotechnology·2026

Same journal

Advances in biomechanical modeling of lumbar spine diseases and tumors: gaps, opportunities, and AI integration.

Frontiers in bioengineering and biotechnology·2026

Same journal

Engineering <i>Lactococcus cremoris</i> strains co-expressing two cellulase genes for growth on cellulose.

Frontiers in bioengineering and biotechnology·2026

Same journal

Exosome-mediated tendon-derived stem cell therapy strategies: potential and challenges.

Frontiers in bioengineering and biotechnology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Deep learning methods for high-resolution microscale light field image reconstruction: a survey.

Bingzhi Lin¹, Yuan Tian², Yue Zhang¹

¹College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

Frontiers in Bioengineering and Biotechnology

|December 3, 2024

Summary

This summary is machine-generated.

Deep learning significantly advances light field microscopy image reconstruction. This review details deep learning methods, classifying them into three types and discussing future challenges for enhanced accuracy and data handling.

Keywords:

deep learning high resolution light field imaging light field microscopy volumetric reconstruction

More Related Videos

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Related Experiment Videos

Last Updated: Jun 6, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Area of Science:

Microscopy
Image Processing
Artificial Intelligence

Background:

Light field microscopy captures 3D scene information.
Image reconstruction is crucial for extracting quantitative data.
Deep learning offers powerful tools for complex image processing tasks.

Purpose of the Study:

To comprehensively review deep learning-based image reconstruction techniques in light field microscopy.
To classify and analyze different deep learning approaches for light field reconstruction.
To identify current challenges and future directions in the field.

Main Methods:

Introduction to light field concepts and deep learning.
Discussion of deep learning applications in light field image reconstruction.
Classification of reconstruction algorithms into three categories based on deep learning integration.

Main Results:

Categorization of deep learning methods: fully deep learning-based, deep learning-enhanced numerical inversion, and numerical inversion with deep learning-enhanced resolution.
Analysis of the features and contributions of each algorithmic type.
Identification of key challenges in deep learning for light field microscopy.

Conclusions:

Deep learning is a vital tool for advancing light field microscopy image reconstruction.
Further research is needed to address challenges in accuracy, data acquisition, enhancement, and interpretability.
The reviewed methods offer diverse strategies for improving light field data analysis.