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Related Concept Videos

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

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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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.
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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.
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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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Related Experiment Video

Updated: Jan 4, 2026

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy
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Spectral reflectance recovery using optimal illuminations.

Ying Fu, Yunhao Zou, Yinqiang Zheng

    Optics Express
    |November 6, 2019
    PubMed
    Summary

    This study introduces a novel convolutional neural network (CNN) approach for spectral reflectance recovery. The method optimizes illumination and uses nonlinear representation, outperforming existing techniques for material property analysis.

    Area of Science:

    • Computer Vision
    • Material Science
    • Machine Learning

    Background:

    • Spectral reflectance provides intrinsic material properties crucial for applications like remote sensing and medicine.
    • Current spectral reflectance recovery methods often neglect illumination effects or assume linear reflectance models.

    Purpose of the Study:

    • To develop a simple and efficient convolutional neural network (CNN)-based method for spectral reflectance recovery.
    • To incorporate optimal illumination design and nonlinear spectral reflectance representation.
    • To improve the accuracy of spectral reflectance recovery from RGB or monochromatic images.

    Main Methods:

    • Designed an illumination optimization layer for multiplexing or designing optimal illumination spectra.
    • Developed a data-driven nonlinear representation for spectral reflectance.

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  • Implemented an end-to-end CNN architecture jointly optimizing illuminations and nonlinear reflectance.

    • Main Results:

    • The proposed CNN-based method achieves superior performance compared to state-of-the-art methods.
    • Experimental results on synthetic and real data validate the effectiveness of the approach.
    • Demonstrated the advantages of optimized illumination and nonlinear reflectance modeling.

    Conclusions:

    • The developed method offers a significant advancement in spectral reflectance recovery.
    • Optimal illumination design and nonlinear representation are key to improving accuracy.
    • The approach has broad applicability in fields requiring precise material property analysis.