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This study introduces a new model and algorithm to reduce spatial non-uniformity (NU) noise in infrared (IR) camera images. The method effectively compensates for temperature-dependent noise variations, improving image quality.

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Area of Science:

  • Infrared Imaging Technology
  • Image Processing
  • Sensor Calibration

Background:

  • Uncooled microbolometer-based infrared (IR) cameras suffer from spatial non-uniformity (NU) noise.
  • This NU noise creates fixed patterns in images, and its intensity fluctuates with camera temperature instability.

Purpose of the Study:

  • To develop a novel model and compensation algorithm for spatial NU noise and its temperature-dependent variations.
  • To improve the accuracy and reliability of IR imaging systems.

Main Methods:

  • A model separating NU noise into constant and dynamic (temperature-dependent) components was developed.
  • Offline characterization of NU parameters using a black-body radiator and temperature samples.
  • Online estimation of temperature-dependent variations using Hammerstein-Wiener and pixelwise least mean squares (LMS) estimators.

Main Results:

  • The proposed algorithm effectively compensated for NU noise in long-wave IR camera images.
  • Achieved a root mean square error of less than 0.25 °C.
  • Demonstrated excellent NU correction performance despite a ~15 °C array temperature variation.

Conclusions:

  • The novel model and algorithm provide effective compensation for spatial NU noise and its temperature-dependent fluctuations.
  • This approach significantly enhances the quality of images from uncooled microbolometer-based IR cameras.