Self-supervised Learning and Adaptation for Single Image Dehazing

Self-supervised Learning and Adaptation for Single Image Dehazing

Yudong Liang, Bin Wang, Wangmeng Zuo, Jiaying Liu, Wenqi Ren

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 1137-1143. https://doi.org/10.24963/ijcai.2022/159

Existing deep image dehazing methods usually depend on supervised learning with a large number of hazy-clean image pairs which are expensive or difficult to collect. Moreover, dehazing performance of the learned model may deteriorate significantly when the training hazy-clean image pairs are insufficient and are different from real hazy images in applications. In this paper, we show that exploiting large scale training set and adapting to real hazy images are two critical issues in learning effective deep dehazing models. Under the depth guidance estimated by a well-trained depth estimation network, we leverage the conventional atmospheric scattering model to generate massive hazy-clean image pairs for the self-supervised pre-training of dehazing network. Furthermore, self-supervised adaptation is presented to adapt pre-trained network to real hazy images. Learning without forgetting strategy is also deployed in self-supervised adaptation by combining self-supervision and model adaptation via contrastive learning. Experiments show that our proposed method performs favorably against the state-of-the-art methods, and is quite efficient, i.e., handling a 4K image in 23 ms. The codes are available at https://github.com/DongLiangSXU/SLAdehazing.
Keywords:
Computer Vision: Transfer, low-shot, semi- and un- supervised learning   
Computer Vision: Neural generative models, auto encoders, GANs  
Computer Vision: Machine Learning for Vision
Computer Vision: Applications