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...

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Echo

Echo

The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...

You might also read

Related Articles

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

Sort by

Same author

A Lightweight Hybrid Authentication and Key Agreement Protocol for Decentralized Device-to-Device Communication with Post-Quantum Confidentiality.

Sensors (Basel, Switzerland)·2026

Same author

Evaluating a Wearable-Based Pain Monitoring System in Palliative Cancer Care: Usability and Feasibility Study.

JMIR formative research·2026

Same author

Reliable and Adaptive Probabilistic Forecasting for Event-Driven Water-Quality Time Series Using a Gated Hybrid-Mixture Density Network.

Sensors (Basel, Switzerland)·2025

Same author

AHE-FNUQ: An Advanced Hierarchical Ensemble Framework with Neural Network Fusion and Uncertainty Quantification for Outlier Detection in Agri-IoT.

Sensors (Basel, Switzerland)·2025

Same author

Exploring Near- and Far-Field Effects in Photoplethysmography Signals Across Different Source-Detector Distances.

Sensors (Basel, Switzerland)·2025

Same author

Overview of Embedded Rust Operating Systems and Frameworks.

Sensors (Basel, Switzerland)·2024

Search research articles

Related Experiment Video

Updated: Nov 3, 2025

Dual Raster-Scanning Photoacoustic Small-Animal Imager for Vascular Visualization

Dual Raster-Scanning Photoacoustic Small-Animal Imager for Vascular Visualization

Published on: July 15, 2020

XCycles Backprojection Acoustic Super-Resolution.

Feras Almasri^1,2, Jurgen Vandendriessche¹, Laurent Segers¹

¹Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel, 1050 Brussels, Belgium.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This summary is machine-generated.

This study introduces a new deep learning model for acoustic image super-resolution (SR) and a new dataset. The XCycles BackProjection model significantly improves acoustic image resolution, addressing limitations in current acoustic imaging.

Keywords:

acoustic camera acoustic imaging delay-and-sum beamformer super-resolution

More Related Videos

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

Switchable Acoustic and Optical Resolution Photoacoustic Microscopy for In Vivo Small-animal Blood Vasculature Imaging

Switchable Acoustic and Optical Resolution Photoacoustic Microscopy for In Vivo Small-animal Blood Vasculature Imaging

Published on: June 26, 2017

Related Experiment Videos

Last Updated: Nov 3, 2025

Dual Raster-Scanning Photoacoustic Small-Animal Imager for Vascular Visualization

Dual Raster-Scanning Photoacoustic Small-Animal Imager for Vascular Visualization

Published on: July 15, 2020

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

Switchable Acoustic and Optical Resolution Photoacoustic Microscopy for In Vivo Small-animal Blood Vasculature Imaging

Switchable Acoustic and Optical Resolution Photoacoustic Microscopy for In Vivo Small-animal Blood Vasculature Imaging

Published on: June 26, 2017

Area of Science:

Computer Vision
Acoustic Imaging
Deep Learning

Background:

Visible image super-resolution (SR) using deep neural networks (DNNs) has advanced significantly.
Non-visible light sensors, like acoustic imaging, offer new visualization capabilities but face resolution limitations.
Existing methods lack dedicated datasets and architectures for acoustic image SR.

Purpose of the Study:

To develop a novel deep learning model for acoustic image super-resolution (SR).
To introduce the Acoustic Map Imaging VUB-ULB Dataset (AMIVU) for acoustic SR research.
To address the limitations in acquiring and processing acoustic data for high-resolution imaging.

Main Methods:

Proposed the XCycles BackProjection (XCBP) model, a novel backprojection architecture.
Utilized an iterative correction procedure within each cycle for residual error reconstruction.
Developed and utilized the AMIVU dataset, featuring simulated and real acoustic images at various resolutions.

Main Results:

The XCBP model demonstrated superior performance compared to classical interpolation and feedforward state-of-the-art models.
Achieved significant improvements in acoustic image resolution.
Drastically reduced sub-sampling errors during data acquisition.

Conclusions:

The proposed XCBP model is effective for acoustic image super-resolution.
The AMIVU dataset provides a valuable resource for advancing acoustic imaging research.
This work overcomes key challenges in acoustic SR, paving the way for enhanced non-visible imaging.