Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

547
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
547
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

11.9K
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...
11.9K

You might also read

Related Articles

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

Sort by
Same author

Investigating performance and key factors for real-world deployment of grain image classification using convolutional neural networks.

Scientific reports·2026
Same author

Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification.

PloS one·2025
Same author

CombiANT reader: Deep learning-based automatic image processing tool to robustly quantify antibiotic interactions.

PLOS digital health·2025
Same author

Combining spatial transcriptomics with tissue morphology.

Nature communications·2025
Same author

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.

Nature methods·2025
Same author

A clinical prostate biopsy dataset with undetected cancer.

Scientific data·2025

Related Experiment Video

Updated: Nov 19, 2025

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples
06:47

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

Published on: June 29, 2022

2.4K

TEM image restoration from fast image streams.

Håkan Wieslander1, Carolina Wählby1,2, Ida-Maria Sintorn1,3

  • 1Department of Information Technology, Uppsala University, Uppsala, Sweden.

Plos One
|February 1, 2021
PubMed
Summary

Deep learning enhances transmission electron microscopy (TEM) live image streams by deblurring and denoising low-quality images. This smart acquisition method improves data analysis efficiency for large microscopy datasets.

More Related Videos

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.2K
Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells
11:55

Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells

Published on: May 28, 2021

4.5K

Related Experiment Videos

Last Updated: Nov 19, 2025

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples
06:47

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

Published on: June 29, 2022

2.4K
High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.2K
Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells
11:55

Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells

Published on: May 28, 2021

4.5K

Area of Science:

  • Microscopy
  • Image Analysis
  • Machine Learning

Background:

  • Microscopy imaging generates vast datasets, necessitating efficient acquisition and analysis methods.
  • Transmission electron microscopy (TEM) produces terabytes of data, with analysis often taking hours.
  • Smart acquisition strategies, like continuous low-resolution streaming, can identify valuable regions for high-resolution imaging but yield degraded images.

Purpose of the Study:

  • To explore the potential and limitations of deep learning for deblurring and denoising fast image streams from TEM.
  • To adapt existing deep learning architectures for the specific challenges of TEM image data.

Main Methods:

  • Utilized deep learning approaches, adapting neural network architectures, convolution blocks, and loss functions for TEM data.
  • Applied methods to real datasets of kidney tissue and a calibration grid, comparing low-quality stream images with high-quality settled images.
  • Evaluated generalizability and overfitting using real and synthetic data.

Main Results:

  • Demonstrated successful deblurring and denoising of TEM live image streams using adjusted deep learning models.
  • Quantitative and visual evaluations confirmed the effectiveness of the image restoration techniques.
  • Identified both significant potential and specific limitations in applying deep learning to TEM live streams.

Conclusions:

  • Deep learning offers a promising approach for real-time image restoration in TEM live imaging.
  • Further research and model refinement are needed to overcome limitations and fully leverage this technology for efficient microscopy data acquisition and analysis.