Semi-supervised Image Dehazing Algorithm Based on Multi-prior Constraint and Consistency Regularization

Previous dehazing models trained on synthetic hazy images can not generalize well on real hazy scenes and improve the performance of high- level vision tasks significantly. To resolve this issue, a semisupervised image dehazing based on multi-priors constrain and output consistency regularization is proposed. The algorithm adopts the encoder and decoder network to train on the synthetic and real hazy images by sharing the parameters. Multi prior-based dehazed images are adopted as pseudo labels to constrain the real scene hazy images. Furthermore, to reduce the divergence of different prior-based methods, the dehazing results of the random mix-up real hazy images are regularized to be consistent with the corresponding mix-up of the prior-based dehazed images. Finally, the experiment results demonstrate the performance of the proposed algorithm compared with the state-of-the-art methods.