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Gated residual feature attention network for real-time Dehazing

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Abstract

Images captured under complicated weather conditions, such as haze, often suffer from a noticeable degradation and hamper its practical application. Traditional dehazing methods use various hand-crafted priors to get a clear image; in such cases, the performance is limited owing to unconstrained environment. In order to restore the haze-free image directly, we propose an end-to-end Gated Residual Feature Attention Network (GRFA-Net) that leverages the haze representations through feature restacking and propagation. We design a Feature Attention Residual Block (FARB) as the core of feature extraction, which employs the residual block to extract hierarchical features, and followed by a novel Feature Attention Module (FAM) that adaptively captures the inter-dependencies from channel- and spatial-wise perspectives. Furthermore, we utilize a group structure (GS) to enlarge the receptive field and merge different multi-level features via the gate fusion module (GFM), respectively. Extensive experiments demonstrate that our GRFA-Net can obtain results that are comparable or even better than previous state-of-the-art methods in terms of quantitative and qualitative evaluation metrics. Furthermore, we reduce the computational complexity considerably and obtain a real-time FPS. The code is available: https://github.com/leandepk/GRFA-Net.

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Acknowledgements

This work was supported by the Beijing Key Laboratory of Precision Photoelectric Measuring Instrument and Technology, the National Natural Science Foundation of China (Grant No. 6130119) and the National Natural Science Foundation of China (Grant No. 61475018) and the Winter Olympics Key Project Technology Fund (2018YFF0300804).

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Weichao Yi, Liquan Dong, Ming Liu, Yuejin Zhao, Mei Hui & Lingqin Kong

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  1. Weichao Yi
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  2. Liquan Dong
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  3. Ming Liu
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  4. Yuejin Zhao
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  5. Mei Hui
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  6. Lingqin Kong
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Correspondence to Liquan Dong or Ming Liu.

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Yi, W., Dong, L., Liu, M. et al. Gated residual feature attention network for real-time Dehazing. Appl Intell 52, 17449–17464 (2022). https://doi.org/10.1007/s10489-022-03157-4

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