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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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 Spectrum01:19

IR Spectrum

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
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Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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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.
Different compounds display unique properties due to their...
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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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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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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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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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...
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Noise fingerprint-based infrared fixed pattern noise removal.

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    Area of Science:

    • Digital Imaging
    • Image Processing
    • Sensor Technology

    Background:

    • Fixed pattern noise (FPN) is a prevalent artifact in digital infrared sensors, stemming from manufacturing imperfections.
    • FPN significantly degrades infrared image quality, necessitating effective removal techniques.

    Purpose of the Study:

    • To introduce a novel method for fixed pattern noise removal in infrared images.
    • To develop a denoising network guided by camera-specific noise fingerprints.

    Main Methods:

    • Proposed a noise fingerprint guided denoising network (NFGD-Net) integrating a denoising network with channel-wise attention and a noise fingerprint extraction subnetwork.
    • Employed a Siamese architecture for noise residual capture, Haar discrete wavelet transform-based attention for directional noise feature extraction, and a gated feature modulator for enhanced learning.

    Main Results:

    • NFGD-Net demonstrated superior performance over state-of-the-art methods in both qualitative and quantitative evaluations on infrared image datasets.
    • Achieved a PSNR of 41.61 and SSIM of 0.9862 under specific noise conditions, indicating effective detail preservation and noise suppression.

    Conclusions:

    • The proposed NFGD-Net effectively suppresses fixed pattern noise by utilizing structured camera noise fingerprints.
    • The method successfully restores high-quality infrared images while preserving fine details, offering a significant advancement in infrared image processing.