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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

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 electronic transitions. As a result...
Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
The essential components of a spectrophotometer include a source of electromagnetic radiation, a slot for placing a material to be analyzed, and a...

You might also read

Related Articles

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

Sort by
Same author

Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance.

World neurosurgery·2023
Same author

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions.

Journal of imaging·2023
Same author

Detection improvement of gliomas in hyperspectral imaging of protoporphyrin IX fluorescence - in vitro comparison of visual identification and machine thresholds.

Cancer treatment and research communications·2022
Same author

Articular cartilage optical properties in the near-infrared (NIR) spectral range vary with depth and tissue integrity.

Biomedical optics express·2021
Same author

Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity.

Biomedical optics express·2020
Same author

A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers.

Sensors (Basel, Switzerland)·2020
Same journal

Multi-module collaborative optimization-driven fast speckle correlation imaging in variable environments.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Secrecy performance analysis of NOMA-UWOC systems over a vertically stratified WGG oceanic turbulence channel.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Backscattering of plane waves in a composite system containing a rough surface and anisotropic scatterers.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Aspherical surface construction methods based on extended Jacobi polynomials.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

OCT sidelobe suppression method based on dual-path phase sinusoidal modulation and minimum value fusion.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Optical design concepts using wavelength-selective diffractive optics to enable miniaturized multimodal endoscopic imaging across separated spectral ranges.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
See all related articles

Related Experiment Video

Updated: May 27, 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

Highlight detection and removal from spectral image.

Pesal Koirala1, Paras Pant, Markku Hauta-Kasari

  • 1University of Eastern Finland, School of Computing, Joensuu, Finland. pesal@neo.no

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|November 4, 2011
PubMed
Summary
This summary is machine-generated.

A new constrained spectral unmixing method effectively removes highlights from single spectral images. This technique ensures positive diffuse image spectra, outperforming existing methods like OSP and PPCA.

More Related Videos

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

Related Experiment Videos

Last Updated: May 27, 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

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

Area of Science:

  • Image Processing
  • Remote Sensing
  • Computer Vision

Background:

  • Spectral imaging captures detailed information across numerous wavelengths.
  • Highlight reflections can obscure diffuse reflectance, complicating spectral analysis.
  • Existing methods like OSP and PPCA have limitations in highlight removal.

Purpose of the Study:

  • To develop and evaluate a novel constrained spectral unmixing method for highlight removal in single spectral images.
  • To ensure positive spectra for diffuse reflectance components.
  • To improve the accuracy of diffuse component estimation.

Main Methods:

  • A constrained spectral unmixing approach was developed, enforcing positive fractions summing to one for diffuse and highlight reflections.
  • The spectral power distribution (SPD) of the light source was utilized as the pure highlight spectrum.
  • Automated target generation identified pure diffuse spectra, selecting the one with minimum angle to the measured spectrum projected orthogonally to the SPD.
  • Constrained energy minimization within a finite impulse response linear filter was employed for highlight and diffuse part detection.

Main Results:

  • The proposed method ensures positive spectra for the diffuse image.
  • Constrained spectral unmixing demonstrated superior performance compared to Orthogonal Subspace Projection (OSP) and Probabilistic Principal Component Analysis (PPCA) in visual assessments.
  • The method successfully distinguishes and removes highlight components.

Conclusions:

  • Constrained spectral unmixing is an effective technique for highlight removal in spectral imaging.
  • The method provides accurate diffuse reflectance spectra, crucial for material identification and analysis.
  • This approach offers a significant improvement over existing spectral unmixing techniques for handling specular reflections.