  We offer a practical unpaired learning based image dehazing network from an
unpaired set of clear and hazy images. This paper provides a new perspective to
treat image dehazing as a two-class separated factor disentanglement task, i.e,
the task-relevant factor of clear image reconstruction and the task-irrelevant
factor of haze-relevant distribution. To achieve the disentanglement of these
two-class factors in deep feature space, contrastive learning is introduced
into a CycleGAN framework to learn disentangled representations by guiding the
generated images to be associated with latent factors. With such formulation,
the proposed contrastive disentangled dehazing method (CDD-GAN) employs
negative generators to cooperate with the encoder network to update
alternately, so as to produce a queue of challenging negative adversaries. Then
these negative adversaries are trained end-to-end together with the backbone
representation network to enhance the discriminative information and promote
factor disentanglement performance by maximizing the adversarial contrastive
loss. During the training, we further show that hard negative examples can
suppress the task-irrelevant factors and unpaired clear exemples can enhance
the task-relevant factors, in order to better facilitate haze removal and help
image restoration. Extensive experiments on both synthetic and real-world
datasets demonstrate that our method performs favorably against existing
unpaired dehazing baselines.
