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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 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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Updated: Jan 7, 2026

Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere
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Pseudo-Sample Generation and Self-Supervised Framework for Infrared Dim and Small Target Detection.

Jinxin Guo1, Weida Zhan1, Dehua Huo1

  • 1School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Entropy (Basel, Switzerland)
|December 24, 2025
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Summary
This summary is machine-generated.

This study introduces a new method for generating realistic infrared simulation data by modeling physical degradation processes. This approach improves deep learning models for detecting dim and small targets in real-world scenarios.

Keywords:
image degradationinformation processingpseudo-sample generationself-supervised learningtarget detection

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Area of Science:

  • Computer Vision
  • Infrared Imaging
  • Machine Learning

Background:

  • Infrared dim and small target detection is vital for long-range sensing.
  • Deep learning for this task is limited by the lack of real annotated data.
  • Current synthetic data methods do not accurately reflect real-world infrared imaging physics.

Purpose of the Study:

  • To develop a novel pseudo-sample generation paradigm for infrared target detection.
  • To address the limitations of existing synthetic data generation methods.
  • To improve the performance and generalization of infrared target detection models.

Main Methods:

  • Physics-informed degradation modeling to decouple target and background processes.
  • Information fidelity optimization for reliable degradation modeling.
  • Online grid-based high-order constraints (semantic, structural, grayscale) for dataset generation.
  • A self-supervised detection framework with custom loss functions and evaluation metrics.

Main Results:

  • Generated synthetic data shows superior authenticity compared to existing methods.
  • The proposed method significantly enhances the generalization performance of various detectors.
  • Achieved superior detection accuracy on real-world data compared to baseline models.

Conclusions:

  • The physics-informed pseudo-sample generation paradigm effectively creates high-fidelity infrared simulation data.
  • This approach overcomes the data scarcity issue in infrared dim and small target detection.
  • The method offers a promising solution for improving real-world infrared sensing applications.