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

Super-resolution Fluorescence Microscopy01:37

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 developed.

You might also read

Related Articles

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

Sort by
Same author

Ensembling Unets for Rare Chromosomal Aberration Detection in Metaphase Images, Uncertainty Quantification, and Ionizing Radiation Dose Estimation.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same author

Cancer-associated fibroblast subtypes differentially modulate natural killer cells in cancer.

Cell reports·2026
Same author

TRIM21 induces selective autophagy of viruses and bacteria.

Molecular cell·2026
Same author

Supervised contrastive learning for cell stage classification of animal embryos.

Scientific reports·2026
Same author

Making the invisible visible: A global examination of careers and recognition for Imaging Scientists in core facilities.

Journal of microscopy·2026
Same author

Protocol for quality control screening of brain organoid morphology.

STAR protocols·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Patch-based nonlocal functional for denoising fluorescence microscopy image sequences.

Jérôme Boulanger1, Charles Kervrann, Patrick Bouthemy

  • 1Radon Institute for Computational and Applied Mathematics, 4040 Linz, Austria. jerome.boulanger@oeaw.ac.at

IEEE Transactions on Medical Imaging
|November 11, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to reduce noise in 3-D fluorescence microscopy images. The technique enhances image quality by leveraging 3-D+time data, improving signal-to-noise ratio for clearer biological observations.

More Related Videos

Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Related Experiment Videos

Last Updated: Jun 18, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Area of Science:

  • Microscopy and Imaging Science
  • Biophysics
  • Image Processing

Background:

  • Fluorescence microscopy generates 3-D+time image sequences crucial for biological research.
  • These images are often corrupted by Poisson-Gaussian noise, which degrades signal-to-noise ratio (SNR) and obscures fine details.
  • Existing denoising methods may struggle to preserve critical space-time discontinuities, such as fast-moving cellular structures.

Purpose of the Study:

  • To develop a nonparametric regression method for denoising 3-D fluorescence microscopy image sequences.
  • To improve the signal-to-noise ratio (SNR) of images affected by Poisson-Gaussian noise.
  • To preserve important space-time discontinuities, like high-velocity moving spots, within the denoised images.

Main Methods:

  • Applied a variance stabilization transform to stabilize image intensity data.
  • Developed a statistical patch-based framework utilizing nonlocal energy functional minimization.
  • Employed spatially-varying neighborhoods with optimized sizes for weighted averaging of image data.
  • The method does not require explicit motion estimation.

Main Results:

  • Successfully reduced Poisson-Gaussian noise in 3-D+time fluorescence microscopy images.
  • Demonstrated preservation of space-time discontinuities, including fast-moving spots.
  • Achieved improved SNR and image quality on both synthetic and real biological image sequences.
  • Validated performance using qualitative and quantitative evaluation criteria.

Conclusions:

  • The proposed nonparametric regression method effectively denoises 3-D fluorescence microscopy image sequences.
  • The statistical patch-based approach preserves crucial dynamic features while enhancing image clarity.
  • This technique offers a valuable tool for analyzing complex biological processes in microscopy data.