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Related Concept Videos

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 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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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 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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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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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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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.
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An Innovative Approach for Removing Stripe Noise in Infrared Images.

Xiaohang Zhao1,2, Mingxuan Li1, Ting Nie1

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

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

A new multi-level image decomposition method effectively removes stripe noise from infrared images. This approach preserves crucial texture details, outperforming traditional denoising techniques.

Keywords:
Alternating Direction Method of Multipliers (ADMM)infrared imagesmulti-level image decomposition methodmulti-sparse constraint representation (MSCR)stripe noises

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Area of Science:

  • Infrared imaging technology
  • Image processing and computer vision

Background:

  • Infrared detector readout circuits can cause stripe noise, degrading image quality.
  • Existing denoising methods struggle to preserve essential image information.

Purpose of the Study:

  • To develop an advanced method for stripe noise removal in infrared images.
  • To enhance the preservation of texture and edge information during denoising.

Main Methods:

  • Proposed a multi-level image decomposition method based on improved LatLRR (MIDILatLRR).
  • Utilized global low-rank properties for noise and smooth information decomposition.
  • Adaptively decomposed texture information into salient part images.
  • Employed multi-sparse constraint representation (MSCR) and ADMM for stripe noise modeling and calculation.

Main Results:

  • The MIDILatLRR method effectively decomposes stripe noise and smooth information into low-rank components.
  • Texture and edge information are better preserved compared to traditional methods.
  • Experimental comparisons show superior performance in both subjective and objective evaluations.

Conclusions:

  • The proposed MIDILatLRR algorithm is highly effective for stripe noise removal in infrared images.
  • The method significantly improves image quality by preserving fine details.
  • Demonstrated superiority over state-of-the-art denoising algorithms.