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DC-GAN with feature attention for single image dehazing

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Abstract

In recent years, the frequent occurrence of smog weather has affected people’s health and has also had a major impact on computer vision application systems. Images captured in hazy environments suffer from quality degradation and other issues such as color distortion, low contrast, and lack of detail. This study proposes an end-to-end, adversarial neural network-based dehazing technique called DC-GAN that combines Dense and Residual blocks efficiently for improved dehazing performance. In addition, it also consists of channel attention and pixel attention, which can offer more versatility when dealing with different forms of data. The Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used as an enhancement method to correct the short-comings in the original GAN’s cost function and create an improvised loss. Based on the experiment results, the algorithm used in this research can generate sharp images with high image quality. The processed images were simultaneously analyzed using the objective evaluation metrics Peak Signal-to-Noise Ratio and Structural Similarity. The findings from our experiment demonstrate that the dehazing effect is favorable compared to other state-of-the-art dehazing algorithms.

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Availability of data and materials

The NTIRE 2018 and RESIDE datasets are both publicly available on (NTIRE 2018: https://data.vision.ee.ethz.ch/cvl/ntire18/; SOTS: https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0).

Code availability

Code that supports the findings of this study will be available for noncommercial academic purposes and will require a formal code use agreement. Please contact ted.meg1234@mail.nwpu. edu.cn for access.

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Acknowledgements

We want to thank Northwestern Polytechinical University for helping us to conduct research in this area and providing resources.

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  1. School of Software Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an, 710072, Shaanxi, China

    Tewodros Tassew & Nie Xuan

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  1. Tewodros Tassew
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  2. Nie Xuan
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Tewodros Tassew wrote the main manuscript text and Nie Xuan contributed and lead the research and help review the manuscipt.

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Correspondence to Nie Xuan.

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Tewodros Tassew and Nie Xuan contributed equally to this work.

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Tassew, T., Xuan, N. DC-GAN with feature attention for single image dehazing. SIViP 18, 2167–2182 (2024). https://doi.org/10.1007/s11760-023-02877-5

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