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 developed.

You might also read

Related Articles

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

Sort by

Same author

Design of Analytical Methods for Protein Adsorption Characteristics at Material Interfaces.

Langmuir : the ACS journal of surfaces and colloids·2026

Same author

Diagnostic performance of droplet digital PCR for KRAS mutation analysis in pancreatic cancer cytology specimens.

Journal of gastroenterology·2026

Same author

Plasma Biomarkers Associated with Clinical Outcomes of FOLFIRI Plus Ramucirumab in RAS Wild-Type Metastatic Colorectal Cancer: The JACCRO CC-16AR Trial.

Targeted oncology·2026

Same author

Plasticity-led evolution of the gut length in wild medaka.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Cooperative Roles of Pds5a and Pds5b Constrain Long-Range Chromatin Interactions in Vertebrate Embryos.

Development, growth & differentiation·2026

Same author

Linked genetic loci and genotype-dependent temperature effects shape craniofacial morphology in medaka.

G3 (Bethesda, Md.)·2026

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

Search research articles

Related Experiment Video

Updated: Jun 22, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Super-resolution without explicit subpixel motion estimation.

Hiroyuki Takeda¹, Peyman Milanfar, Matan Protter

¹Electrical Engineering Department, University of California, Santa Cruz, CA 95064, USA. htakeda@soe.ucsc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 29, 2009

Summary

This summary is machine-generated.

This study introduces a novel video super-resolution framework using multidimensional kernel regression. It enhances videos with complex motion without needing precise motion estimation, improving optical resolution.

More Related Videos

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

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

Published on: January 6, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Related Experiment Videos

Last Updated: Jun 22, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

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

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

Published on: January 6, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Area of Science:

Computer Vision
Image Processing
Video Enhancement

Background:

Conventional super-resolution requires precise motion estimation, limiting its use to simple video motions.
Complex motion in videos poses a significant challenge for existing super-resolution techniques.

Purpose of the Study:

To develop a novel framework for adaptive enhancement and spatiotemporal upscaling of videos with complex motions.
To overcome the limitations of explicit motion estimation in super-resolution.

Main Methods:

Utilizes multidimensional kernel regression to approximate pixels with 3-D local Taylor series.
Estimates series coefficients via local weighted least-squares, using space-time orientation for weights.
Implicitly captures local motion information through pixel neighborhood comparisons.

Main Results:

Demonstrates super-resolution capabilities on general videos with arbitrary motion.
Achieves improved optical resolution and overall performance.
Significantly broadens the applicability of super-resolution to complex motion sequences.

Conclusions:

The proposed framework effectively handles complex video motions without explicit motion estimation.
This approach offers improved video super-resolution performance and wider applicability.
Enables enhanced optical resolution for diverse video content.