Conditional Generative Adversarial Defogging Algorithm Based on Polarization Characteristics
Jingjing Zhang, Kangsheng Bao, Xin Zhang, Fudong Nian, Teng Li, Yuzhou Zeng · 2021
To overcome image degradation under the conditions of haze, and fog, a method using conditional generative adversarial defogging algorithm based on polarization characteristic is proposed. Four original images with different polarization angles were obtained from the original image, and then the polarization images were characterized using Stokes vectors. From the relationship between Stokes vector and polarization image, each polarization image with different angles is input into the same network to extract features. At the same time, the enhanced network is used to extract the characteristics of the fog area in the polarization image, and the polarization information is extracted by layer jump connection, which is fused with the image features of different angles. By constructing the loss function and obtaining the optimal solution, the fog-free image is finally reconstructed. Experimental results show that a clear image can be reconstructed in fog situations by using conditional generative adversarial network-based on polarization characteristics, and the structural similarity is improved by about 10%, the peak signal to noise ratio is increased by about 0.5.