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

188
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...
188
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

2.8K
Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Trends of HIV, hepatitis C virus and syphilis seroprevalence among injection and non-injection drug users in southwestern China, 2010-2017.

AIDS care·2020
Same author

Chandler-Loop surveyed blood compatibility and dynamic blood triggered degradation behavior of Zn-4Cu alloy and Zn.

Materials science & engineering. C, Materials for biological applications·2020
Same author

Knockdown of Parkinson's disease-related gene ATP13A2 reduces tumorigenesis via blocking autophagic flux in colon cancer.

Cell & bioscience·2020
Same author

Diagnostics of skin features through 3D skin mapping based on electro-controlled deposition of conducting polymers onto metal-sebum modified surfaces and their possible applications in skin treatment.

Analytica chimica acta·2020
Same author

Spongy <i>p</i>-Toluenesulfonic Acid-doped Polypyrrole with Extraordinary Rate Performance as Durable Anodes of Sodium-Ion Batteries at Different Temperatures.

Langmuir : the ACS journal of surfaces and colloids·2020
Same author

NOD1/RIP2 signalling enhances the microglia-driven inflammatory response and undergoes crosstalk with inflammatory cytokines to exacerbate brain damage following intracerebral haemorrhage in mice.

Journal of neuroinflammation·2020

Related Experiment Video

Updated: Jul 18, 2025

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.1K

Hyperspectral Blind Unmixing Using a Double Deep Image Prior.

Chao Zhou, Miguel R D Rodrigues

    IEEE Transactions on Neural Networks and Learning Systems
    |August 21, 2023
    PubMed
    Summary

    This study introduces an unsupervised deep learning framework for hyperspectral image (HSI) unmixing. The method ensures physically meaningful results for both linear and nonlinear blind unmixing problems.

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Hyperspectral image (HSI) unmixing is crucial for analyzing spectral data.
    • Existing machine learning methods often lack physically meaningful unmixing results.
    • Guidance is needed for accurate endmember and abundance extraction.

    Purpose of the Study:

    • To propose an unsupervised framework for linear and nonlinear blind hyperspectral image unmixing.
    • To ensure physically meaningful unmixing results using deep learning.
    • To improve the performance of HSI unmixing algorithms.

    Main Methods:

    • A novel unsupervised framework inspired by deep image prior (DIP).
    • Three modules: Endmember estimation using DIP (EDIP), Abundance estimation using DIP (ADIP), and a Mixing Module (MM).

    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

    2.5K
    Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and &lambda; Hyperspectral FRET Imaging and Analysis
    08:22

    Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

    Published on: October 27, 2020

    3.9K

    Related Experiment Videos

    Last Updated: Jul 18, 2025

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    00:07

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.1K
    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

    2.5K
    Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and &lambda; Hyperspectral FRET Imaging and Analysis
    08:22

    Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

    Published on: October 27, 2020

    3.9K
  • A composite loss function and an adaptive loss weight strategy for nonlinear scenarios.

    • Main Results:

    • The proposed framework successfully performs both linear and nonlinear blind unmixing.
    • Achieved superior performance compared to state-of-the-art unmixing algorithms.
    • Demonstrated effectiveness on both synthetic and real-world hyperspectral datasets.

    Conclusions:

    • The DIP-inspired unsupervised framework provides a robust solution for HSI unmixing.
    • The adaptive loss weight strategy enhances performance in complex nonlinear mixing scenarios.
    • This approach offers a significant advancement in extracting meaningful information from hyperspectral data.