Non-Uniformity Correction of Infrared Images Using Histogram Statistics and Processing Methods

Through histogram statistical analysis and corresponding processing techniques, we have successfully accomplished the task of infrared image non-uniformity correction. The implementation involves calculating image histograms to analyze pixel value distributions, followed by applying correction algorithms to normalize intensity variations. In this process, we specifically addressed the common stripe non-uniformity issue in infrared images by developing specialized filtering and equalization methods. By analyzing and processing the images using histogram-based algorithms, we effectively eliminated these stripes, thereby improving image quality and accuracy. The core methodology includes histogram equalization techniques and adaptive filtering algorithms that dynamically adjust correction parameters based on statistical characteristics. The application scope of this technology is extensive, playing significant roles across various domains of infrared image processing and analysis. Through our research and experiments involving multiple infrared image datasets, we have validated the effectiveness of this approach using quantitative metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). Our work provides important references for future research, including potential implementations using Python/OpenCV or MATLAB's image processing toolbox with functions like histeq() and adaptive histogram equalization. We believe further development of this technology will bring more advancements and innovations to the infrared image processing field, particularly through machine learning-enhanced histogram processing techniques.