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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

702
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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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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Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption.

Jinyuan Liu, Guanyao Wu, Zhu Liu

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    This survey provides a comprehensive overview of deep learning-based infrared-visible image fusion (IVIF) methods. It organizes recent advancements, analyzes challenges, and discusses future research directions in this critical computer vision task.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Infrared-visible image fusion (IVIF) integrates infrared and visible spectra for enhanced representation.
    • Deep learning has significantly advanced IVIF since 2018, introducing diverse networks and loss functions.
    • Existing research lacks a recent, comprehensive survey of learning-based IVIF methodologies.

    Purpose of the Study:

    • To provide a comprehensive survey of the rapidly developing field of learning-based IVIF.
    • To organize and elucidate prevalent IVIF methodologies using a multi-dimensional framework.
    • To address critical issues such as data compatibility, perception accuracy, and efficiency.

    Main Methods:

    • Introduction of a multi-dimensional framework for classifying learning-based IVIF methods.
    • In-depth analysis of new approaches with a detailed lookup table for core ideas.
    • Quantitative and qualitative performance comparisons across registration, fusion, and high-level tasks.

    Main Results:

    • A structured overview of deep learning-based IVIF strategies, from basic enhancement to advanced extensions.
    • Detailed analysis and categorization of numerous IVIF approaches.
    • Summarized performance metrics highlighting strengths and weaknesses of different methods.

    Conclusions:

    • The survey addresses the need for organized knowledge in the evolving field of IVIF.
    • Identifies key challenges and potential future research avenues in learning-based image fusion.
    • Offers a valuable resource for researchers and practitioners in computer vision and image processing.