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A dual branch network combining detail information and color feature for remote sensing image dehazing

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Abstract

Remote sensing images are often affected by haze, resulting in problems such as blurriness, loss of details, and color casts. To effectively remove haze and obtain high-quality remote sensing image, a dual branch network, named DICFNet, that effectively combines detail information and color features is proposed. Specifically, a detail information learning branch is designed firstly, which uses the detail feature residual extraction module (DFREM) to capture the detail features and promote feature learning. Secondly, to learn comprehensive color features, a color feature learning branch is designed. It converts the RGB color space into the Lab color space that is very similar to human visual perception, and then puts the color feature extraction module (CFEM) into use to learn brightness and saturation features. Finally, a learnable fusion module is adopted to obtain the optimal fusion scheme for the previous two branches, enhancing the ability of the model to produce clear remote sensing images. A wealth of experimental evidence indicates that the proposed DICFNet outperforms comparison methods in both visual quality and quantitative evaluation while maintaining a lower memory footprint and requiring fewer computational resources. In addition, detailed ablation experiments demonstrate the effectiveness of the core components of the model.

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Acknowledgements

Not applicable.

Funding

This research is funded by the Anhui Province Higher Education Natural Science Research Project (Nos. 2023AH053088, 2023AH051609), the National Natural Science Foundation of China (No. 62066039), the Natural Science Foundation of Qinghai Province of China (No. 2022-ZJ-925), the Technology Innovation Platform Project of Chuzhou Polytechnic (No. YJP-2023-02), the Anhui Provincial Quality Engineering Project for Higher Education Institutions (Nos. 2022jyxm1147, 2022jnds043), the Chuzhou Polytechnic Campus Research Project (No. ZKZ-2022-02).

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Authors and Affiliations

  1. The College of Computer, Qinghai Normal University, No. 38 Wusi West Road, Chengxi District, Xining, 810008, China

    Mengjun Miao, Heming Huang & Kedi Huang

  2. School of Information Engineering, Chuzhou Polytechnic, No. 2188 Fengle Avenue, Nanqiao District, Chuzhou, 239000, China

    Mengjun Miao & Shanqin Wang

  3. The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining, 810008, China

    Mengjun Miao, Heming Huang & Kedi Huang

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  1. Mengjun Miao
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Contributions

All authors have made contributions to this work. Individual contributions are as follows: M. M. contributed to conceptualization, methodology, software, writing-original draft preparation, and funding acquisition; H. H., K. H., S. W. writing-review and editing, and funding acquisition. All authors read and approved the final manuscript.

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Correspondence to Heming Huang.

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Miao, M., Huang, H., Huang, K. et al. A dual branch network combining detail information and color feature for remote sensing image dehazing. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02388-w

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