Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing
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arXiv
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| Format: | Preprint |
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2025
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| author | Su, Xiongfei Li, Siyuan Cui, Yuning Cao, Miao Zhang, Yulun Chen, Zheng Wu, Zongliang Wang, Zedong Zhang, Yuanlong Yuan, Xin |
| author_facet | Su, Xiongfei Li, Siyuan Cui, Yuning Cao, Miao Zhang, Yulun Chen, Zheng Wu, Zongliang Wang, Zedong Zhang, Yuanlong Yuan, Xin |
| contents | Image dehazing is a crucial task that involves the enhancement of degraded images to recover their sharpness and textures. While vision Transformers have exhibited impressive results in diverse dehazing tasks, their quadratic complexity and lack of dehazing priors pose significant drawbacks for real-world applications.
In this paper, guided by triple priors, Bright Channel Prior (BCP), Dark Channel Prior (DCP), and Histogram Equalization (HE), we propose a \textit{P}rior-\textit{g}uided Hierarchical \textit{H}armonization Network (PGH$^2$Net) for image dehazing. PGH$^2$Net is built upon the UNet-like architecture with an efficient encoder and decoder, consisting of two module types: (1) Prior aggregation module that injects B/DCP and selects diverse contexts with gating attention. (2) Feature harmonization modules that subtract low-frequency components from spatial and channel aspects and learn more informative feature distributions to equalize the feature maps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01136 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing Su, Xiongfei Li, Siyuan Cui, Yuning Cao, Miao Zhang, Yulun Chen, Zheng Wu, Zongliang Wang, Zedong Zhang, Yuanlong Yuan, Xin Computer Vision and Pattern Recognition Image dehazing is a crucial task that involves the enhancement of degraded images to recover their sharpness and textures. While vision Transformers have exhibited impressive results in diverse dehazing tasks, their quadratic complexity and lack of dehazing priors pose significant drawbacks for real-world applications. In this paper, guided by triple priors, Bright Channel Prior (BCP), Dark Channel Prior (DCP), and Histogram Equalization (HE), we propose a \textit{P}rior-\textit{g}uided Hierarchical \textit{H}armonization Network (PGH$^2$Net) for image dehazing. PGH$^2$Net is built upon the UNet-like architecture with an efficient encoder and decoder, consisting of two module types: (1) Prior aggregation module that injects B/DCP and selects diverse contexts with gating attention. (2) Feature harmonization modules that subtract low-frequency components from spatial and channel aspects and learn more informative feature distributions to equalize the feature maps. |
| title | Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.01136 |