MT-Net: Single image dehazing based on meta learning, knowledge transfer and contrastive learning

Single image dehazing is becoming increasingly important as its results impact the efficiency of subsequent computer vision tasks. While many methods have been proposed to address this challenge, existing dehazing approaches often exhibit limited adaptability to different types of images and lack future learnability. In light of this, we propose a dehazing network based on meta-learning, knowledge transfer, and contrastive learning, abbreviated as MT-Net. In our approach, we combine knowledge transfer with meta-learning to tackle these challenges, thus enhancing the network’s generalization performance. We refine the structure of knowledge transfer by introducing a two-phases approach to facilitate learning under the guidance of teacher networks and learning committee networks. We also optimize the negative examples of contrastive learning to reduce the contrast space. Extensive experiments conducted on synthetic and real datasets demonstrate the remarkable performance of our method in both quantitative and qualitative comparisons. The code has been released on https://github.com/71717171fan/MT-Net.