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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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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.
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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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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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    Area of Science:

    • Image processing
    • Computer vision
    • Remote sensing

    Background:

    • Infrared images are vital for reconnaissance, security, and fire detection.
    • Stripe noise, caused by detector limitations, significantly degrades infrared image quality.
    • Effective stripe noise removal is crucial for reliable image analysis.

    Purpose of the Study:

    • To propose a novel low-rank decomposition model for infrared stripe noise removal.
    • To differentiate stripe noise from essential image information for targeted suppression.
    • To preserve structural features of infrared images during the destriping process.

    Main Methods:

    • A low-rank decomposition model is developed to separate stripe noise.
    • Column gradient domain low-rank prior and weighted group sparsity are used for noise components.
    • Structure-aware gradient sparsity prior is applied to preserve image information.
    • Iterative solutions incorporate column difference minimization and variable acceleration for convergence.

    Main Results:

    • The proposed method effectively separates stripe noise from image information.
    • Experimental comparisons show superior performance over existing destriping algorithms.
    • Both subjective and objective evaluations confirm the method's effectiveness.

    Conclusions:

    • The novel low-rank decomposition model offers a superior approach to infrared stripe noise removal.
    • The method successfully suppresses noise while preserving critical image structures.
    • This advancement benefits various applications requiring high-quality infrared imagery.