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

Multiple Pyramids Based Image Inpainting Using Local Patch Statistics and Steering Kernel Feature.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2019
Same author

Dense RGB-D Semantic Mapping with Pixel-Voxel Neural Network.

Sensors (Basel, Switzerland)·2018
Same author

Learning a Patch Quality Comparator for Single Image Dehazing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2018
Same author

A Group-Based Image Inpainting Using Patch Refinement in MRF Framework.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2017
Same author

Clustering with Hypergraphs: The Case for Large Hyperedges.

IEEE transactions on pattern analysis and machine intelligence·2017
Same author

Efficient Globally Optimal Consensus Maximisation with Tree Search.

IEEE transactions on pattern analysis and machine intelligence·2016
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: May 21, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Super resolution image reconstruction through Bregman iteration using morphologic regularization.

Pulak Purkait1, Bhabatosh Chanda

  • 1Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India. pulak_r@isical.ac.in

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 2, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonlinear regularization method using multiscale morphology for edge-preserving super-resolution image reconstruction. The approach effectively reduces noise during image processing and reconstruction.

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

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Related Experiment Videos

Last Updated: May 21, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

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

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

  • Image Processing
  • Computer Vision
  • Computational Imaging

Background:

  • Multiscale morphological operators are widely used for image processing and feature extraction.
  • Super-resolution (SR) image reconstruction aims to enhance image detail from low-resolution inputs.
  • Noise is a common issue in low-resolution image formation and SR reconstruction.

Purpose of the Study:

  • To develop a nonlinear regularization method for edge-preserving super-resolution image reconstruction.
  • To model SR as a deblurring problem and solve it using Bregman iterations.
  • To efficiently suppress noise during SR image estimation.

Main Methods:

  • Modeling super-resolution as a deblurring problem.
  • Applying a nonlinear regularization method based on multiscale morphology.
  • Utilizing Bregman iterations to solve the inverse problem.

Main Results:

  • The proposed method effectively preserves edges during super-resolution reconstruction.
  • The algorithm demonstrates efficient suppression of noise inherent in low-resolution images.
  • Experimental results validate the effectiveness of the regularization and reconstruction technique.

Conclusions:

  • The developed multiscale morphological regularization method is effective for edge-preserving SR image reconstruction.
  • The approach successfully mitigates noise issues in SR image processing.
  • This work contributes a robust technique for enhancing image resolution while maintaining structural integrity.