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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.
The ATR process begins by directing a beam...
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Infrared (IR) Spectroscopy: Overview01:09

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
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IR Frequency Region: Fingerprint Region01:03

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

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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...
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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

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In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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Related Experiment Video

Updated: Nov 1, 2025

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
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Infrared image impulse noise suppression using tensor robust principal component analysis and truncated total

Yan Zhang, Yuyi Shao, Jinyue Shen

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    This study introduces an efficient infrared image denoising method using tensor robust principal component analysis to suppress impulse noise. The new approach preserves image details and significantly enhances computational speed for high-resolution infrared images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Signal Processing

    Background:

    • Infrared image denoising is crucial but existing methods blur details and are slow for large images.
    • Impulse noise suppression in infrared images presents challenges in preserving information and efficiency.

    Purpose of the Study:

    • To develop an efficient infrared image denoising method for impulse noise suppression.
    • To overcome limitations of existing methods regarding detail preservation and computational speed.

    Main Methods:

    • Tensor robust principal component analysis (TRPCA) framework.
    • Randomized tensor singular-value thresholding algorithm for solving tensor kernel norm.
    • Image blocking technique combined with truncated total variation (TTV).

    Main Results:

    • The proposed method effectively suppresses impulse noise while preserving image details.
    • Significant improvement in computational efficiency for large-resolution infrared images.
    • Enhanced smoothness of denoised images through truncated total variation.

    Conclusions:

    • The novel TRPCA-based method offers superior denoising performance and efficiency compared to state-of-the-art techniques.
    • The algorithm successfully addresses the trade-off between denoising quality and computational cost.
    • This method is highly effective for practical applications requiring high-resolution infrared image processing.