Abstract
Existing image dehazing methods consider the learning-based methods as the mainstream. Most of them are trained on synthetic dataset, and may not be able to efficiently transfer to real outdoor scenes. In order to further improve the dehazing effect of the model in real outdoor scenes, this paper proposes an end-to-end Multi-Feature Fusion Network for Single Image Dehazing (MFFN). The proposed network combines the prior-based methods and learning-based methods. This paper first uses the method of supporting backpropagation in order to directly extract the dark channel prior and color attenuation prior features. It then designs a Multi-Feature Adaptive Fusion Module (MFAFM) which can adaptively fuse and enhance the two prior features. Finally, the prior features are added to the decoding stage of the backbone network in a multi-scale manner. The experimental results on the synthetic dataset and real-world dataset demonstrate that the proposed model performs favorably against the state-of-the-art dehazing algorithms.
Supported by Yulin science and technology plan project CXY-2020-07.
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Luo, J., Chang, T., Bo, Q. (2022). Multi-feature Fusion Network for Single Image Dehazing. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_11
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