Infrared polarization image has excellent applications in target detection, tracking and warning, etc., but the original infrared polarization image has problems such as cold reflection and line blurring, which results in the low quality of the final image and affects the overall effect of infrared polarization detection. To address the above shortcomings, we propose an improved image correction method based on single-pixel non-uniformity differential image correction to remove the cold reflection. In addition, we use the two-dimensional discrete cosine transform method to Sharpen the image. The results show that the proposed method can effectively suppress the cold reflection problem and improve the robustness of the image.
Because of the shortcomings of traditional infrared-polarization image fusion algorithm, such as low intelligence and single optimization index, this paper proposes an intelligent infrared-polarization image fusion optimization algorithm based on fireworks algorithm. Based on the strong complementarity between infrared-intensity image and degree of linearpolarization (DOLP) image and the explosive optimization of fireworks algorithm, the problem model of weighted fusion algorithm is established, and the fitness function based on root mean square error (RMSE) is constructed to calculate the optimal weight of source image. In the fusion experiment of long-wave infrared-intensity image and DOLP image, this method is compared with the common fusion algorithms. The results show that this method can effectively fuse the infrared-intensity and degree of polarization information, and the evaluation indexes of standard deviation, spatial frequency, mutual information, structural similarity, peak signal-to-noise ratio and information entropy of the fusion image are better than the comparison algorithm. In the future, cooperated with the long-wave infrared-polarization imaging system, this method can be applied to improve the infrared detection ability in complex environment.