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A deep hourglass-structured fusion model for efficient single image dehazing

  • 1204: Multimedia Technology for Security and Surveillance in Degraded Vision
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Abstract

Hazy images obstruct the visibility of image content, which can negatively affect vision-based decision-making in multimedia systems and applications. Recently, convolutional neural networks (CNN) are proven with great benefit to remove single image haze, which has aroused research attention. However, in practice, previous works fail to fully exploit multi-scale features and restore the faithful image details from the hazy inputs, resulting in sub-optimal performance. In this paper, we propose a novel and high-efficiency deep hourglass-structured fusion model to address this issue, which also indicates the applicability of the modified hourglass architecture to remove haze. Unlike the conventional multi-scale learning schemes, top-down and bottom-up feature fusions are repeated, so each of the coarse-to-fine scale representations receives data of parallel ones, which allows for more flexible information exchange and aggregation at various scales. To be specific, we develop residual dense module as the backbone unit, while introducing the channel-wise attention mechanism to further enhance the representation ability of the network. As proved by extensive assessments demonstrate, our designed model outclasses existing ones and achieves the advanced performance on benchmark datasets and real hazy images. We have released source codes on GitHub: https://github.com/cxtalk/Hourglass-DehazeNet.

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Authors and Affiliations

  1. College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, 110135, China

    Yufeng Li, Xiang Chen, Caihua Kong, Longgang Dai & Yufeng Huang

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  1. Yufeng Li
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  2. Xiang Chen
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  3. Caihua Kong
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  4. Longgang Dai
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  5. Yufeng Huang
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Correspondence to Xiang Chen.

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Li, Y., Chen, X., Kong, C. et al. A deep hourglass-structured fusion model for efficient single image dehazing. Multimed Tools Appl 81, 35247–35260 (2022). https://doi.org/10.1007/s11042-022-12312-5

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