Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing

• URL: http://arxiv.org/abs/2503.01136v1
• Date: Mon, 03 Mar 2025 03:36:30 GMT
• Title: Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing
• Authors: Xiongfei Su, Siyuan Li, Yuning Cui, Miao Cao, Yulun Zhang, Zheng Chen, Zongliang Wu, Zedong Wang, Yuanlong Zhang, Xin Yuan,
• Abstract summary: We propose a textitPrior-textitguided textitHarmonization Network (PGH$2$Net) for image dehazing.<n>PGH$2$Net is built upon the UNet-like architecture with an efficient encoder and decoder, consisting of two module types.
• Score: 50.92820394852817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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