Polarization image fusion algorithm based on global information correction

The paper proposes a fusion framework for getting more information from multi-dimensional polarization image. Overall, the challenge lies on overcoming the information loss arising from reflection/irradiation interference of polarizers, inherent defects of intensity images and improper distribution of fusion weights in most fusion processes. So we introduce a modified front polarizer system model, Tiansi mask operator and comprehensive weights. We start our methodology with the modified front polarizer system model, aiming to correct the polarization information. Then, we make use of the high- frequency information enhancement effect and low frequency information preservation ability of Tiansi operator, combined with adaptive histogram equalization (AHE) to achieve intensity enhancement. Finally, the contrast, saliency and exposedness weights of the source images are respectively calculated by using Laplace filtering, IG algorithm, Gauss model and weighting them to obtain the comprehensive weights. We obtain the final image by the fusion of the processed image and the corresponding weight coefficients. Experimental results show that our method has good visual effects and is beneficial to target detection.