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

Deconvolution01:20

Deconvolution

165
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...
165
Classification of Systems-I01:26

Classification of Systems-I

190
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
190
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Molecular and Functional Diversity of Crustin-Like Genes in the Shrimp <i>Litopenaeus vannamei</i>.

Marine drugs·2020
Same author

The Relationship Between Hormone Replacement Therapy and Risk of Kidney Cancer in Women: A Meta-Analysis.

Cancer control : journal of the Moffitt Cancer Center·2020
Same author

Comparison of the cytoplastic genomes by resequencing: insights into the genetic diversity and the phylogeny of the agriculturally important genus Brassica.

BMC genomics·2020
Same author

Impact of Aeromonas hydrophila and infectious spleen and kidney necrosis virus infections on susceptibility and host immune response in Chinese perch (Siniperca chuatsi).

Fish & shellfish immunology·2020
Same author

Pathogenicity of Aeromonas hydrophila causing mass mortalities of Procambarus clarkia and its induced host immune response.

Microbial pathogenesis·2020
Same author

Upregulation of miR-133a by adiponectin inhibits pyroptosis pathway and rescues acute aortic dissection.

Acta biochimica et biophysica Sinica·2020

Related Experiment Video

Updated: Jul 11, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.9K

Image denoising and segmentation model construction based on IWOA-PCNN.

Xiaojun Zhang1

  • 1College of Software Technology, Henan Finance University, Zhengzhou, 450000, China. castorly@hafu.edu.cn.

Scientific Reports
|November 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved whale optimization algorithm (IWOA) to enhance pulse coupled neural networks (PCNN) for superior image denoising and segmentation. The IWOA-PCNN model significantly boosts image quality and processing efficiency.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

435

Related Experiment Videos

Last Updated: Jul 11, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

435

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Signal Processing

Background:

  • Pulse Coupled Neural Networks (PCNN) exhibit limitations in image denoising and segmentation due to complex structures and suboptimal performance.
  • Existing methods often struggle to balance noise reduction with information preservation in image processing.

Purpose of the Study:

  • To enhance the performance of Pulse Coupled Neural Networks (PCNN) for image denoising and segmentation.
  • To develop an optimized PCNN model using a novel metaheuristic algorithm.

Main Methods:

  • A multi-strategy collaborative improvement whale optimization algorithm (WOA) was developed, termed Improved WOA (IWOA).
  • IWOA was employed to determine optimal parameter values for the PCNN, creating the IWOA-PCNN model.
  • The IWOA-PCNN model was evaluated for its effectiveness in image denoising and segmentation tasks.

Main Results:

  • The IWOA-PCNN model demonstrated superior image denoising performance, producing crisper images with preserved information.
  • Achieved an average Peak Signal-to-Noise Ratio (PSNR) of 35.87 and Mean Squared Error (MSE) of 0.24.
  • Outperformed WTGAN and IGA-NLM models in processing time, with average NU and D values indicating enhanced quality.

Conclusions:

  • The proposed IWOA-PCNN method effectively enhances PCNN capabilities for image denoising and segmentation.
  • The optimized model offers significant improvements in image quality metrics and processing speed.
  • This advancement encourages further development and application of PCNN in image processing.