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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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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.
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A Novel Stripe Noise Removal Model for Infrared Images.

Mingxuan Li1,2, Shenkai Nong1,2, Ting Nie1

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

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This study introduces a novel method to remove streak noise from infrared images. The approach effectively preserves image details and outperforms existing techniques in experimental comparisons.

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adaptive edge-preserving operator (AEPO)alternating direction method of multipliers (ADMM)infrared imagesstripe noises

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Area of Science:

  • Image Processing
  • Infrared Imaging
  • Signal Denoising

Background:

  • Streak noise in infrared images degrades quality due to detector non-uniformity.
  • This noise complicates subsequent image analysis and processing tasks.

Purpose of the Study:

  • To develop an effective algorithm for eliminating streak noise in infrared images.
  • To preserve essential image details and edges during the denoising process.

Main Methods:

  • Utilized differences between stripe noise and image components.
  • Employed gradient sparsity and global sparsity as regularization terms.
  • Introduced an adaptive edge-preserving operator (AEPO) and ADMM for optimization.

Main Results:

  • The proposed method successfully removed streak noise from infrared images.
  • AEPO effectively protected image edges, preventing detail loss.
  • Experimental results showed superiority over state-of-the-art methods.

Conclusions:

  • The novel approach offers a superior solution for infrared streak noise removal.
  • The method enhances image quality while preserving critical edge information.