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

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by

Same author

Phase unwrapping in digital holographic interferometry via physics-informed convolutional-Fourier neural network.

Optics express·2026

Same author

Acoustic emission monitoring of damage modes in reinforced concrete beams by using narrow partial power bands.

Scientific reports·2024

Same author

Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated cycle-consistent generative adversarial network.

Applied optics·2024

Same author

Does Atmospheric Corrosion Alter the Sound Quality of the Bronze Used for Manufacturing Bells?

Materials (Basel, Switzerland)·2023

Same author

Damage Localization on Composite Structures Based on the Delay-and-Sum Algorithm Using Simulation and Experimental Methods.

Sensors (Basel, Switzerland)·2023

Same author

Speckle denoising based on deep learning via a conditional generative adversarial network in digital holographic interferometry.

Optics express·2022

Search research articles

Related Experiment Video

Updated: Sep 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Learning Network for Speckle De-Noising in Severe Conditions.

Marie Tahon¹, Silvio Montrésor², Pascal Picart^2,3

¹LIUM (Laboratoire d'Informatique de l'Université du Mans), Le Mans Université, Avenue Olivier Messiaen, 72085 Le Mans, France.

Journal of Imaging

|June 23, 2022

Summary

This study introduces a new database for de-noising algorithms in digital holography, improving phase fringe image quality. Deep neural networks trained on diverse noise conditions enhance performance in challenging holographic interferometry applications.

Keywords:

DnCNN database controlled parameters deep learning digital holography fine-tuning image de-noising

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Related Experiment Videos

Last Updated: Sep 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Area of Science:

Optics
Image Processing
Metrology

Background:

Digital holography enables precise object modification measurement.
Digital holographic interferometry analyzes phase changes but is hindered by speckle noise.
Effective de-noising is crucial for accurate phase data in holographic interferometry.

Purpose of the Study:

To address speckle noise in de-noising algorithms for digital holographic interferometry.
To introduce a novel database of simulated phase fringe images for algorithm evaluation.
To train and assess deep neural network (DNN) models for enhanced phase map de-noising.

Main Methods:

Development of a new database with controllable speckle grain size and fringe noise levels.
Training of deep neural network architectures using phase maps with varied noise characteristics.
Evaluation of DNN models using metrics like PSNR, phase error, perceived quality index, and peak-to-valley ratio.

Main Results:

DNN models trained on diverse noise conditions demonstrate improved efficiency and robustness.
The proposed database facilitates the evaluation of de-noising algorithms under severe noise.
Trained models show enhanced generality for de-noising phase maps with significant noise.

Conclusions:

Diverse training data significantly improves the performance and generalizability of de-noising algorithms in digital holography.
The developed database serves as a valuable resource for advancing de-noising techniques in holographic interferometry.
This work contributes to more reliable phase measurements in digital holography, even under noisy conditions.