An Algorithm for Infrared Polarized Light Imaging Band Preference under Matrix Fourier Optics Optimization

In infrared polarized light imaging, the thermal noise and environmental interference components are usually distributed in the high-frequency part, while the useful signals are usually concentrated in the low-frequency part. In order to accurately select the optimal infrared polarized light imaging band, an infrared polarized light imaging band optimization algorithm based on matrix Fourier optics optimization is proposed. By introducing the principle of Fourier optics, a filter is designed to identify the factors that affect the image quality. Combined with eigenvalue analysis, data dimensionality reduction and transmission difference function evaluation, the optimized infrared polarized light imaging data are preprocessed to select the optimal band which is most sensitive to the surface characteristics and material properties of the target object. The experimental results show that the research method can significantly improve the image contrast, clarity and reduce noise under different light intensity and exposure time, so as to comprehensively improve the image quality.