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

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

You might also read

Related Articles

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

Sort by

Same author

3D bone printing via ultrasound-mediated osteogenic differentiation of stem cells (3DBonUS).

Biofabrication·2026

Same author

α-Synuclein and γ-Tubulin Cooperatively Regulate Activity-Evoked Presynaptic Microtubule Nucleation to Gate Dopamine Release.

bioRxiv : the preprint server for biology·2026

Same author

Iloperidone treatment mitigates the Juvenile Huntington's Disease phenotype possibly via Sigma-1 Receptor Modulation.

The FEBS journal·2026

Same author

Modeling ALS in a dish: how organoids are transforming research.

Frontiers in medicine·2026

Same author

Missense mutations on SynGAP C2 domain impair membrane diffusion.

Protein science : a publication of the Protein Society·2026

Same author

Preventing Pathogenic Dimerization in a Misfolded Antibody Light Chain through the Design of an Inhibitory Peptide.

The journal of physical chemistry. B·2026

Same journal

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor.

Optics express·2026

Same journal

Robust 3D topography measurement of large-range high-aspect-ratio structures based on dual-domain statistical filtering in SD-OCT.

Optics express·2026

Same journal

Broadband transmissive terahertz metasurface for simultaneous quad-mode OAM multiplexing.

Optics express·2026

Same journal

Leveraging two-dimensional materials for high-sensitivity optical sensors: quasi-bound states in the continuum within hybrid metasurfaces.

Optics express·2026

Same journal

Resolution investigation for dual-spherical-wave optical scanning holographic microscopy: methods and performance.

Optics express·2026

Same journal

Robustness of parallel subnetwork-filtered diffractive deep neural networks.

Optics express·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 6, 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

Physics-informed deep neural network for image denoising.

Emmanouil Xypakis, Valeria de Turris, Fabrizio Gala

|January 5, 2024

Summary

This summary is machine-generated.

We developed a novel deep neural network (DNN) for image enhancement that improves signal-to-noise ratio by leveraging photon detection statistics. This method surpasses existing algorithms, especially for high-dynamic-range images, without arbitrary data normalization.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Related Experiment Videos

Last Updated: Jul 6, 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 Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Deep neural networks (DNNs) are used for image enhancement, improving signal-to-noise ratio and resolution.
Current DNN algorithms often rely on arbitrary data normalization, affecting performance across different datasets.
Existing methods struggle with high-dynamic-range images due to normalization dependencies.

Purpose of the Study:

To develop a DNN algorithm for superior image signal-to-noise ratio enhancement.
To overcome the limitations of arbitrary normalization in existing image enhancement techniques.
To create a robust DNN model that accounts for the statistical nature of photon detection.

Main Methods:

Developed a novel deep neural network (DNN) algorithm for image enhancement.
The model is based on the Poissonian statistics inherent in photon detection processes.
Algorithm utilizes distance between probability functions, not just count-rate, for enhancement.
Input data is transformed from camera count-rate to photon-number, avoiding arbitrary renormalization.

Main Results:

The developed DNN algorithm significantly enhances image signal-to-noise ratio.
Performance surpasses existing image enhancement algorithms.
Achieved high performance, particularly with high-dynamic-range images.
The method is robust and does not require arbitrary image renormalization.

Conclusions:

A new DNN-based image enhancement method has been developed, leveraging photon detection statistics.
The algorithm offers improved performance over existing methods, especially for challenging image types.
This approach provides a more principled and effective way to enhance optical image quality.