Deep Dehazing Network With Latent Ensembling Architecture and Adversarial Learning.

Most existing dehazing algorithms recover haze-free image by solving the hazy imaging model using estimated transmission map and global atmospheric light. However, inaccurate estimation of these variables and the strong assumptions of imaging model result in unrealistic dehazing results. In this paper, we use the adversarial game between a pair of neural networks to accomplish end-to-end photo-realistic dehazing.