Hap-Net: Single Image Dehazing Based on Hybrid Attention and Physical Prior Model

Existing dehazing methods use spatial attention to tackle the uneven distribution of haze at the image level and channel attention to model channel-wise differences, which ignore information interaction between them. Therefore, we introduce a hybrid attention module to solve this issue. Moreover, we incorporate a pixel attention module afterward to enhance image details. In addition, we introduce a physical prior module, based on the Atmospheric Scattering Model, to improve the interpretability of the dehazing process. By combining the abovementioned components, we propose our hybrid attention and physical prior network (HAP-Net) for recovering haze-free images. Extensive experimental results demonstrate the effectiveness of our HAP-Net.

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Publication Details

Published
2024-11-01
DOI
https://doi.org/10.1109/icacr62205.2024.11053727
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Hap-Net: Single Image Dehazing Based on Hybrid Attention and Physical Prior Model

Yunfei Hu, Yanduo Zhang, Shihao Chen, Tao Lü et al.
Image Enhancement Techniques
article

Hap-Net: Single Image Dehazing Based on Hybrid Attention and Physical Prior Model

Yunfei Hu, Yanduo Zhang, Shihao Chen, Tao Lü, Jiaming Wang
article en

Abstract

Existing dehazing methods use spatial attention to tackle the uneven distribution of haze at the image level and channel attention to model channel-wise differences, which ignore information interaction between them. Therefore, we introduce a hybrid attention module to solve this issue. Moreover, we incorporate a pixel attention module afterward to enhance image details. In addition, we introduce a physical prior module, based on the Atmospheric Scattering Model, to improve the interpretability of the dehazing process. By combining the abovementioned components, we propose our hybrid attention and physical prior network (HAP-Net) for recovering haze-free images. Extensive experimental results demonstrate the effectiveness of our HAP-Net.

Wuhan Institute of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hubei Province
Openalex Percentile: Top 70%
Image Enhancement Techniques
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