Yong Liu,
Qingji Dong,
Chao Zhu,
Yu Guo,
Fei Wang
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Existing dehazing methods face challenges in generalization due to the lack of paired real-world training data and tailored models. Recently, some semi-supervised/unsupervised schemes have been explored and achieve impressive performance. However, their performance still depends heavily on synthetic training data and the introduced prior-based strong constraints do not always hold. In this paper, we first introduce RealHQ-HAZE, a new dataset with 200 collected real-world hazy images, corresponding 200 carefully rendered haze-free images, and additional 1000 varicolored hazy images transferred from the collected images. We also propose a Prior-compensated Multi-stage Dehazing Network (PMDN), which can learn different levels of real-world haze distribution through multi-stage progressive learning. To utilize prior knowledge effectively, we introduce a Prior-based Feature Compensation Module (PFCM), guiding intermediate results with an adaptive weight. Additionally, we propose a MixCut Consistent Dehazing (MCCD) strategy to mix paired and derived images using a cross-cutting scheme, reinforcing dehazing through consistency principles. Extensive experiments demonstrate the effectiveness of our dataset and the superior performance of PMDN over existing state-of-the-art dehazing methods.
- 2025.07.15: Create this repository.