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

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

443
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
443
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

120
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....
120
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.7K
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...
7.7K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.1K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.1K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

270
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
270
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

490
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
490

You might also read

Related Articles

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

Sort by
Same author

Benchmarking AI scientists for omics data-driven biological discovery.

Bioinformatics (Oxford, England)·2026
Same author

Development of a novel GAK solution for efficient hypothermic preservation of NK cell viability and anti-tumor function.

Stem cell research & therapy·2026
Same author

Correction: Calpain inhibition as a novel therapeutic strategy for aortic dissection with acute lower extremity ischemia.

Molecular medicine (Cambridge, Mass.)·2026
Same author

Extensive frontal cortex abnormalities and cognitive impairment in methamphetamine use disorder: a comprehensive 3T structural MRI analysis.

BMC psychiatry·2026
Same author

Induction of stress granules alleviates programmed cell death induced by lysosomal damage during NK cell cryopreservation.

Cell death discovery·2026
Same author

Beyond carrier frequency: a preliminary multicenter study of simultaneous couple-based comprehensive carrier screening for common and rare genetic disorders.

Genome medicine·2026

Related Experiment Video

Updated: Aug 5, 2025

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

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery.

Yifan Xiong1, Guangyuan Wu1, Xiaozhou Li2

  • 1Faculty of Light Industry, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized spectral recovery method using subspace merging for improved accuracy. The new approach enhances representative sample selection for better spectral reflectance estimation.

Keywords:
camera responsesrepresentative samplesspectral recoverysubspace merging

More Related Videos

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K
Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
09:57

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Published on: July 25, 2022

4.0K

Related Experiment Videos

Last Updated: Aug 5, 2025

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
Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K
Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
09:57

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Published on: July 25, 2022

4.0K

Area of Science:

  • Computer Vision
  • Color Science
  • Image Processing

Background:

  • Spectral reflectance recovery is crucial for accurate color reproduction.
  • Existing sample selection methods neglect subspace merging, impacting performance.
  • RGB trichromatic values are the input for the proposed method.

Purpose of the Study:

  • To propose an optimized spectral recovery method incorporating subspace merging.
  • To improve the selection of representative training samples for spectral recovery.
  • To enhance the accuracy of spectral reflectance estimation.

Main Methods:

  • Each training sample is treated as a subspace.
  • Subspaces are merged based on Euclidean distance.
  • Iterative merging and subspace tracking determine sample locations.
  • Nearest distance principle selects representative training samples.

Main Results:

  • The proposed method achieves high spectral and colorimetric accuracy.
  • The method demonstrates effective representative sample selection.
  • Performance is validated under various illuminants and cameras.

Conclusions:

  • The subspace merging approach significantly improves spectral recovery.
  • The method offers a more accurate and efficient way to select representative samples.
  • This technique advances spectral reflectance estimation accuracy.