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Updated: Oct 22, 2025

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Thermal Image Restoration Based on LWIR Sensor Statistics.

Jaeduk Han1, Haegeun Lee2, Moon Gi Kang2

  • 1Samsung Electronics, Suwon-si 16677, Gyeonggi-do, Korea.

Sensors (Basel, Switzerland)
|August 28, 2021
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Summary

This study derives natural statistics for thermal imaging sensors and proposes a novel restoration method. The optimized algorithm significantly enhances thermal image quality, outperforming conventional techniques.

Keywords:
deconvolutionimage optimizationlong wage infraredregularizationtotal variation

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Area of Science:

  • Image processing and computer vision
  • Infrared imaging technology
  • Statistical modeling

Background:

  • Visible light images utilize natural statistics for restoration, but thermal images (long-wavelength infrared - LWIR) have unique degradations like residual nonuniformity (RNU).
  • Existing thermal image processing rarely incorporates natural statistics, despite studies on thermal image properties.

Purpose of the Study:

  • To derive natural statistics specific to thermal imaging sensors.
  • To develop and validate an optimization method for restoring degraded thermal images using these derived statistics.

Main Methods:

  • Analysis of high-frequency components in thermal images across various datasets using statistical measures (e.g., correlation coefficient, KL divergence).
  • Design of cost functions based on validated natural statistics.
  • Pixel-wise optimization method to minimize the designed cost functions for image restoration.

Main Results:

  • Generalized natural statistical properties of thermal images were derived.
  • The proposed pixel-wise optimization algorithm demonstrated superior performance compared to conventional methods.
  • Quantitative assessments using PSNR, SSIM, Ro, and ERo indices confirmed the effectiveness of the restoration method.

Conclusions:

  • Natural statistics can be effectively incorporated into thermal image processing for improved restoration.
  • The developed method offers a specialized and high-performing solution for thermal image enhancement.
  • The findings provide a foundation for future research in statistically-driven thermal imaging.