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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.0K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

245
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
245
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

316
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
316

You might also read

Related Articles

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

Sort by
Same author

An integrated review of monkeypox: from pathogen and epidemiology to diagnostics, control, and challenges.

Emerging microbes & infections·2026
Same author

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Formation Mechanism of Microstructure in NiCr Coatings by Plasma Spray Melting.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

A novel antisense lncRNA, LPCRL, functions as a molecular scaffold for the USP15/MIB1 complex to promote primary cisplatin resistance and tumor progression in lung squamous cell carcinoma.

Journal of experimental & clinical cancer research : CR·2026
Same author

Risk factors for diabetic at-risk foot disease among the diabetes over 60 years old: a cross-sectional study.

Frontiers in medicine·2026
Same author

PSMD11 stabilizes PGM3 by antagonizing Parkin to promote bladder cancer progression through energy metabolism reprogramming.

Cell death & disease·2026

Related Experiment Video

Updated: Jan 1, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

2.9K

Local model-based hyperspectral pansharpening algorithm via optimization constraint equation and sliding window.

Wenqian Dong, Song Xiao, Jiahui Qu

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |December 25, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel local model-based pansharpening method to overcome over-sharpening and blurring issues in fused images. The new approach enhances detail preservation and reduces redundancy for improved hyperspectral and panchromatic image fusion.

    More Related Videos

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.7K
    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    9.0K

    Related Experiment Videos

    Last Updated: Jan 1, 2026

    ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
    07:11

    ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

    Published on: August 19, 2021

    2.9K
    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.7K
    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    9.0K

    Area of Science:

    • Remote Sensing
    • Image Processing
    • Computer Vision

    Background:

    • Injection model-based algorithms are common for pansharpening.
    • Existing methods struggle with over-sharpening and image blurring.
    • Hyperspectral (HS) and Panchromatic (PAN) image fusion is crucial for detailed analysis.

    Purpose of the Study:

    • To propose a local model-based pansharpening method.
    • To address the imbalance between over-sharpening and blurring in fused images.
    • To improve the fusion performance of HS and PAN images.

    Main Methods:

    • Developed an optimization constraint equation using a quality index.
    • Reduced detail differences between HS and PAN images.
    • Introduced a novel sliding-window-based fusion scheme for adaptive detail fusion and redundancy reduction.

    Main Results:

    • The proposed algorithm effectively reduces the difference between HS and PAN image details.
    • The sliding-window scheme adaptively fuses details, minimizing redundancy.
    • Simulation experiments demonstrate excellent fusion performance, validated by subjective and objective metrics.

    Conclusions:

    • The local model-based pansharpening method significantly improves fusion quality.
    • The proposed optimization and fusion scheme effectively balances detail preservation and noise reduction.
    • This method offers a superior solution for pansharpening challenges in hyperspectral imaging.