Recursive algorithms for bias and gain nonuniformity correction in infrared videos
Daniel R Pipa1, Eduardo A B da Silva, Carla L Pagliari
1Universidade Federal do Rio de Janeiro, Rio de Janeiro 21945-970, Brazil. danielpipa@ieee.org
Summary
New algorithms reduce fixed-pattern noise (FPN) in infrared focal-plane array (IRFPA) detectors. These scene-based methods continuously compensate for sensor nonuniformity, improving infrared image quality and fidelity.
Area of Science:
- Optics and Photonics
- Image Processing
- Sensor Technology
Background:
- Infrared focal-plane array (IRFPA) detectors are crucial for imaging applications.
- Fixed-pattern noise (FPN), or spatial nonuniformity, significantly degrades IRFPA image quality.
- Existing FPN correction methods struggle to keep pace with technological advancements.
Purpose of the Study:
- To develop novel scene-based algorithms for continuous compensation of bias and gain nonuniformity in IRFPA sensors.
- To address the persistent challenge of FPN in infrared imaging.
- To enhance the fidelity and quality of infrared images.
Main Methods:
- Proposed algorithms utilize recursive least-square and affine projection techniques.
- These methods jointly compensate for both bias and gain nonuniformities at the pixel level.
- The algorithms are designed for rapid convergence and robustness against noise.
Main Results:
- Experimental results with synthetic and real IRFPA videos demonstrate significant FPN reduction.
- The proposed algorithms show competitive performance compared to state-of-the-art methods.
- Recovered images exhibit higher fidelity, indicating improved image quality.
Conclusions:
- The developed scene-based correction algorithms effectively mitigate FPN in IRFPA detectors.
- The proposed techniques offer a robust and efficient solution for enhancing infrared image quality.
- These advancements contribute to more reliable infrared imaging systems.
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