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Related Concept Videos

Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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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 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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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 Image Deconvolution Considering Fixed Pattern Noise.

Haegeun Lee1, Moon Gi Kang1

  • 1School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.

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|March 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to improve infrared image quality by addressing both fixed pattern noise (FPN) and blurring simultaneously. The method enhances thermal imaging for industrial applications by jointly removing these common degradations.

Keywords:
fixed pattern noiseinfrared imagenon-blind deconvolutionnon-uniformity correctionoptimizationregularization

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Area of Science:

  • Optics and Photonics
  • Image Processing
  • Thermal Imaging

Background:

  • Industrial demand for high-quality thermal information is rising.
  • Existing infrared image enhancement methods often address fixed pattern noise (FPN) or blurring artifacts independently.
  • Real-world infrared images frequently exhibit both FPN and blurring, necessitating a joint approach.

Purpose of the Study:

  • To develop an infrared image deconvolution algorithm that jointly handles fixed pattern noise (FPN) and blurring artifacts.
  • To create a unified framework for improving the quality of infrared images degraded by multiple factors.

Main Methods:

  • Derived an infrared linear degradation model encompassing thermal information acquisition system degradations.
  • Developed a strategy for precise estimation of column FPN components, robust to random noise.
  • Proposed a non-blind image deconvolution scheme based on infrared image gradient statistics.

Main Results:

  • The proposed algorithm successfully removes both FPN and blurring artifacts from infrared images.
  • Experimental verification confirmed the algorithm's superiority in joint artifact removal.
  • The developed framework accurately models real-world infrared imaging systems.

Conclusions:

  • Jointly addressing FPN and blurring in a single framework is crucial for effective infrared image enhancement.
  • The proposed deconvolution algorithm offers a significant advancement for thermal imaging quality.
  • The method provides a more realistic reflection of actual infrared imaging system performance.