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

12.2K
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...
12.2K

You might also read

Related Articles

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

Sort by
Same author

Phase stabilization for long-baseline interferometry of incoherent optical sources.

Optics letters·2026
Same author

Super-resolving binary-source hypothesis testing with a double-clad fiber coupler.

Optics letters·2025
Same author

Uplink pre-compensation for ground-to-space optical communications using downlink tomographic reconstruction.

Optics express·2025
Same author

Spatial mode demultiplexing for super-resolved source parameter estimation.

Optics express·2025
Same author

Rapid tactical deployment capability of a transportable optical ground station.

Scientific reports·2025
Same author

Feasibility of phase stabilization for satellite-mediated twin-field quantum key distribution.

Optics letters·2025
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. Super-resolution Parameter Estimation Using Machine Learning-assisted Spatial Mode Demultiplexing.
  1. Home
  3. Super-resolution Parameter Estimation Using Machine Learning-assisted Spatial Mode Demultiplexing.

Related Experiment Video

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

8.8K

Super-Resolution Parameter Estimation Using Machine Learning-Assisted Spatial Mode Demultiplexing.

David R Gozzard1,2, John S Wallis1, Alex M Frost1

  • 1International Centre for Radio Astronomy Research, The University of Western Australia, Crawley, WA 6009, Australia.

Sensors (Basel, Switzerland)
|September 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new machine learning model estimates light source separation below the diffraction limit using spatial mode demultiplexing (SPADE) imaging. This technique offers sub-diffraction resolution for astronomical applications.

Keywords:
machine learningquantum imagingsuper-resolution

More Related Videos

Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

10.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.8K

Related Experiment Videos

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

8.8K
Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

10.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.8K

Area of Science:

  • Optics and Photonics
  • Machine Learning Applications
  • Computational Imaging

Background:

  • Resolving fine details in optical imaging is often limited by the diffraction limit.
  • Spatial mode demultiplexing (SPADE) offers a potential pathway to overcome these limitations.
  • Machine learning (ML) presents opportunities for advanced data analysis in complex imaging scenarios.

Purpose of the Study:

  • To develop and evaluate a machine learning model for estimating the separation and relative brightness of closely spaced light sources.
  • To assess the model's performance in achieving sub-diffraction limit resolution.
  • To explore the application of ML-assisted SPADE imaging for overcoming diffraction limitations.

Main Methods:

  • Utilized a multi-planar light converter (MPLC) to perform SPADE imaging.
  • Trained, validated, and tested a lightweight machine learning model on experimental laboratory data.
  • Focused on estimating source separation and relative brightness below the diffraction limit.

    • Main Results:

    • The ML model accurately estimated source separation up to two orders of magnitude below the diffraction limit for comparable brightness sources.
    • Achieved accurate sub-diffraction separation resolution even when source brightness differed by four orders of magnitude.
    • Performance was limited by cross-talk within the MPLC.

    Conclusions:

    • ML-assisted SPADE imaging demonstrates significant potential for achieving sub-diffraction resolution.
    • The developed ML model shows promise for applications in astronomical imaging.
    • Further improvements in MPLC technology could enhance the capabilities of this technique.