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

12.1K
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...
12.1K

You might also read

Related Articles

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

Sort by
Same author

Precise estimation of rice leaf macro and micro nutrients from multi-spectral images using neural architecture search with polynomial approximation functions.

Scientific reports·2026
Same author

A robust stacked ensemble strategy with multi-optimizer CNN models for skin cancer classification.

Scientific reports·2026
Same author

Maximum-Return-Driven Consensus Framework With Internal-External Compensation in Undirected Collaboration Network.

IEEE transactions on cybernetics·2026
Same author

Data-driven explainable chronic kidney disease detection using RF based data imputation and meta-ensemble learning.

Scientific reports·2026
Same author

Resilient Consensus Control of Nonlinear Multiagent Systems Under Hybrid Cyberattacks: A Disturbance Observer-Based Neural Network Approach.

IEEE transactions on cybernetics·2026
Same author

Application of fractal theory in cancer detection: A review.

Bio Systems·2026

Related Experiment Video

Updated: Dec 16, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.5K

Multi-focus image fusion using fractal dimension.

Chinmaya Panigrahy, Ayan Seal, Nihar Kumar Mahato

    Applied Optics
    |July 2, 2020
    PubMed
    Summary

    This study introduces a novel translation-invariant multi-focus image fusion method using the à-trous wavelet transform. The approach enhances image clarity and outperforms existing methods in subjective and objective assessments.

    More Related Videos

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
    13:01

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

    Published on: April 10, 2016

    34.5K
    Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
    05:24

    Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

    Published on: January 10, 2025

    679

    Related Experiment Videos

    Last Updated: Dec 16, 2025

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.5K
    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
    13:01

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

    Published on: April 10, 2016

    34.5K
    Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
    05:24

    Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

    Published on: January 10, 2025

    679

    Area of Science:

    • Computer Vision
    • Digital Image Processing

    Background:

    • Multi-focus image fusion combines partially focused images into a fully focused one.
    • Existing transform-domain methods often suffer from translation variance, impacting texture and edge preservation.
    • Translation-invariant transforms simplify fusion rule design by producing consistent approximation and detail images.

    Purpose of the Study:

    • To develop a novel translation-invariant multi-focus image fusion method.
    • To utilize the à-trous wavelet transform for enhanced fusion performance.
    • To introduce fractal dimension and Otsu's threshold for improved clarity and detail fusion.

    Main Methods:

    • A translation-invariant approach using the à-trous wavelet transform was developed.
    • Fractal dimension was employed as a clarity measure for approximation coefficients.
    • Otsu's thresholding was used to fuse the detail coefficients.

    Main Results:

    • The proposed method demonstrated competitive and superior performance compared to nine state-of-the-art fusion techniques.
    • Both subjective and objective assessments, using eight quality metrics, validated the method's effectiveness.
    • The approach proved effective for both grayscale and color multi-focus image pairs.

    Conclusions:

    • The à-trous wavelet transform-based translation-invariant fusion method offers significant advantages.
    • The integration of fractal dimension and Otsu's thresholding enhances fusion quality.
    • The proposed method represents a valuable advancement in multi-focus image fusion technology.