A generative adversarial network incorporating dark channel prior loss used for single image defogging

Single image defogging using generative adversarial networks (GAN) relies on annotated datasets, which is easy to cause over-fitting of ground truth, and usually performs not well on natural images. To solve this problem, this paper designed a GAN network incorporating dark channel prior loss to defogging single image. This prior loss can influence the model prediction results in network training and correct the sparsity and skewness of the dark channel feature map. At the same time, it can definitely improve the actual defogging effect and prevent the model from over-fitting problem. In addition, in order to solve the problem that the extraction method of traditional dark channel feature has non-convex function and is difficult to be embedded into network training, this paper introduces a new extraction strategy which compresses pixel values instead of minimum filtering. The implementation function of this strategy is a convex function, which is conducive to embedded network training and enhances the overall robustness of the algorithm. Moreover, this strategy does not need to set a fixed scale to extract the dark channel feature map, and has good adaptability to images with different resolutions. Experimental results show that the proposed algorithm performs better on real images and synthetic test-sets like SOTS when compared with other sota algorithms.