Semi-supervised single image dehazing based on dual-teacher-student network with knowledge transfer

While significant progress has been made in image dehazing techniques, the lack of large-scale labeled datasets remains one of the limiting factors for enhancing the performance of image dehazing algorithms. Therefore, based on the mean teacher model, we propose a semi-supervised dehazing framework with a dual-teacher-student (DTS) architecture. DTS is composed of a pretrained teacher network (P-teacher), a mean teacher network (M-teacher), and a student network. The P-teacher facilitates the student network in learning intermediate layer features that resemble haze-free images through knowledge transfer. The M-teacher guides the student network in image dehazing in unsupervised manner. The P-teacher, M-teacher, and student networks share the same network architecture known as the multiscale feature fusion attention-enhanced network (MFFA-Net). The MFFA-Net consists of a multiscale feature fusion network (MFF-Net) and an attention network (A-Net). The MFF-Net is responsible for fusing features from different levels. The A-Net is capable of compensating for information loss during downsampling in the MFF-Net and dynamically adjusting the focus on different regions. Extensive experimental results demonstrate that the dehazing method proposed in this paper outperforms several state-of-the-art algorithms on multiple datasets.The code has been released on https://github.com/houqianwen/MFFA-Net.