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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

399
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....
399
Deconvolution01:20

Deconvolution

629
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...
629
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.6K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.6K
Downsampling01:20

Downsampling

721
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...
721
Upsampling01:22

Upsampling

658
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
658
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

382
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
382

You might also read

Related Articles

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

Sort by
Same author

Triptolide protects rat heart against pressure overload-induced cardiac fibrosis.

International journal of cardiology·2013
Same author

Multiresidue pesticide analysis of botanical dietary supplements using salt-out acetonitrile extraction, solid-phase extraction cleanup column, and gas chromatography-triple quadrupole mass spectrometry.

Analytical chemistry·2013
Same author

Interaction domains of p62: a bridge between p62 and selective autophagy.

DNA and cell biology·2013
Same author

Predictors of seizure freedom after surgical management of tuberous sclerosis complex: a systematic review and meta-analysis.

Epilepsy research·2013
Same author

Temporary ileostomy versus colostomy for colorectal anastomosis: evidence from 12 studies.

Scandinavian journal of gastroenterology·2013
Same author

Localized leptin release may be an important mechanism of curcumin action after acute ischemic injuries.

The journal of trauma and acute care surgery·2013

Related Experiment Video

Updated: Feb 23, 2026

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

2.9K

Learning Low-Rank Decomposition for Pan-Sharpening With Spatial-Spectral Offsets.

Shuyuan Yang, Kai Zhang, Min Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |September 1, 2017
    PubMed
    Summary

    This study introduces a novel low-rank pan-sharpening (LRP) model using offset learning to accurately fuse multispectral and panchromatic images. The method effectively reduces spatial and spectral distortions for improved image quality.

    More Related Videos

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    18.2K
    Super-resolution Imaging of Neuronal Dense-core Vesicles
    09:30

    Super-resolution Imaging of Neuronal Dense-core Vesicles

    Published on: July 2, 2014

    10.1K

    Related Experiment Videos

    Last Updated: Feb 23, 2026

    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

    2.9K
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    18.2K
    Super-resolution Imaging of Neuronal Dense-core Vesicles
    09:30

    Super-resolution Imaging of Neuronal Dense-core Vesicles

    Published on: July 2, 2014

    10.1K

    Area of Science:

    • Remote Sensing
    • Image Processing
    • Computer Vision

    Background:

    • Pan-sharpening aims to enhance multispectral image resolution using a panchromatic image.
    • Accurate injection component identification is crucial for effective pan-sharpening.
    • Existing methods often struggle with spatial and spectral distortions.

    Purpose of the Study:

    • To develop a novel low-rank pan-sharpening (LRP) model based on offset learning.
    • To reduce spatial and spectral distortions in fused high-resolution multispectral (HRMS) images.
    • To improve the robustness of pan-sharpening against noise and outliers.

    Main Methods:

    • A low-rank pan-sharpening (LRP) model is proposed using offset learning.
    • Two offsets are defined to capture spatial and spectral differences.
    • Spatial equalization and spectral proportion constraints are applied to the offsets.
    • A spatial and spectral constrained stable low-rank decomposition algorithm is developed using augmented Lagrange multiplier.

    Main Results:

    • The proposed LRP method effectively reduces spatial and spectral distortions.
    • The method demonstrates robustness in handling noise and outliers in source images.
    • Experimental results on various datasets validate the efficiency of the LRP model.

    Conclusions:

    • The offset learning perspective offers a new approach to pan-sharpening.
    • The developed LRP model achieves simultaneous reduction of spatial and spectral distortions.
    • The method provides an efficient and effective solution for high-quality image fusion.