Polarization imaging shadow removal based on attention conditional generative adversarial networks

Shadows in polarized images often interfere with the acquisition and analysis of the polarization state of light. By removing the shadows, these interferences can be eliminated, and the polarization information can be extracted more accurately for subsequent processing. To solve the problem of insufficient illumination and information recovery in the shadow region of polarization images, we propose an attention condition generative adversarial network (GAN) for shadow removal in polarized imaging. The method uses conditional GAN s as its basic framework and introduces attention modules into the generator, which enhances the network's ability to localize and recognize shadows. At the same time, the polarization shadow removal images from different directions are fused to maximize the generation of polarization shadow-free images. The acquisition of shadow images in four different scenarios uses a polarization camera. The proposed method is compared with the recent method. Experimental results show that the polarization image after shadow removal using our method is closer to the real ground image, and the evaluation index is better than other methods.