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学者姓名：苏志鹏
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Abstract ：
Transformers have been widely used in image dehazing tasks due to their powerful self-attention mechanism for capturing long-range dependencies. However, directly applying Transformers often leads to coarse details during image reconstruction, especially in complex real-world hazy scenarios. To address this problem, we propose a novel Hybrid Attention Encoder (HAE). Specifically, a channel-attention-based convolution block is integrated into the Swin-Transformer architecture. This design enhances the local features at each position through an overlapping block-wise spatial attention mechanism while leveraging the advantages of channel attention in global information processing to strengthen the network's representation capability. Moreover, to adapt to various complex hazy environments, a high-quality codebook prior encapsulating the color and texture knowledge of high-resolution clear scenes is introduced. We also propose a more flexible Binary Matching Mechanism (BMM) to better align the codebook prior with the network, further unlocking the potential of the model. Extensive experiments demonstrate that our method consistently outperforms the second-best methods by a margin of 8% to 19% across multiple metrics on the RTTS and URHI datasets. The source code has been released at https://github.com/HanyuZheng25/HADehzeNet.
Keyword ：
Channel attention Channel attention Discrete codebook learning Discrete codebook learning Single image dehazing Single image dehazing Swin-transformer Swin-transformer
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| GB/T 7714 | Huang, Liqin , Zheng, Hanyu , Pan, Lin et al. Codebook prior-guided hybrid attention dehazing network [J]. | IMAGE AND VISION COMPUTING , 2025 , 162 . |
| MLA | Huang, Liqin et al. "Codebook prior-guided hybrid attention dehazing network" . | IMAGE AND VISION COMPUTING 162 (2025) . |
| APA | Huang, Liqin , Zheng, Hanyu , Pan, Lin , Su, Zhipeng , Wu, Qiang . Codebook prior-guided hybrid attention dehazing network . | IMAGE AND VISION COMPUTING , 2025 , 162 . |
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