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

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Abstract

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.

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Data availability

The datasets are both publicly available on(SOTS [37], I-HAZE/O-HAZE [38, 39], BeDDE [40]).

Code Availability

The code has been released on https://github.com/houqianwen/MFFA-Net.

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Funding

This study was funded by the National Natural Science Foundation of China (Grant No. 61773243).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Qufu Normal University, No.57 Jingxuan West Road, Qufu, Jining, 273165, China

    Jianlei Liu, Qianwen Hou, Shilong Wang & Xueqing Zhang

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  1. Jianlei Liu
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  2. Qianwen Hou
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  3. Shilong Wang
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  4. Xueqing Zhang
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Contributions

Jianlei Liu was responsible for the entire project process. Jianlei Liu and Shilong Wang designed the entire network architecture. Jianlei Liu and Qianwen Hou conducted experiments and wrote the manuscript text. Xueqing Zhang was in charge of preparing the dataset. All authors reviewed the manuscript.

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Correspondence to Jianlei Liu.

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Liu, J., Hou, Q., Wang, S. et al. Semi-supervised single image dehazing based on dual-teacher-student network with knowledge transfer. SIViP (2024). https://doi.org/10.1007/s11760-024-03216-y

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