Statistical Algorithm-Based Infrared Image Non-Uniformity Correction
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The paper presents an infrared image non-uniformity correction algorithm based on statistical methods. This scene-based algorithm achieves real-time correction by analyzing non-uniformity patterns in infrared imagery. The implementation typically involves calculating statistical metrics like mean/variance across image regions and applying adaptive filtering techniques to correct fixed-pattern noise. Through histogram equalization and pixel-response normalization, the algorithm enhances image accuracy and clarity by compensating for sensor-induced variations. Such correction is crucial in infrared image processing, significantly improving image quality and visual interpretation. This statistical approach demonstrates substantial practical potential, particularly in thermal imaging systems requiring real-time performance, where it can be implemented efficiently using parallel processing techniques on FPGA or GPU architectures.
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