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Underwater image enhancement combining low-dimensional and global features

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Abstract

The physical transformation of light will cause the quality of underwater images to decrease, which impacts the precision of object detection, recognition, and segmentation in underwater circumstances. In this study, we suggest an underwater image enhancement combining low-dimensional and global features (UIELG). This model can availably heighten the texture minutiae and global key features of subaquatic images. Moreover, we advise a new loss function, which can effectively boost the structure and texture similarity of underwater images. Finally, we train and test the model on the synthetic subaquatic images. The experimental outcomes declare that this model is preferable to the existing models in both SSIM and PSNR scores. And the experimental outcomes on the real-world subaquatic image dataset present the generalization and robustness of the suggested model.

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Acknowledgements

There is no financial support for this work. The authors declare that no conflicts of interest exist.

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The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received.

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Tongji University, No. 4800, Cao’an Highway, Shanghai, 201804, SH, China

    Nianzu Qiao

  2. Xian Jiaotong University, No.28, West Xianning Road, Xi’an, Shaanxi, 710049, SN, China

    Lamei Di

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  1. Nianzu Qiao
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  2. Lamei Di
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NQ hereby confirm on behalf of all authors that This manuscript, or a large part of it, has not been published, was not, and is not being submitted to any other journal. All authors each made a significant contribution to the research reported and have read and approved the submitted manuscript.

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Correspondence to Nianzu Qiao.

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Qiao, N., Di, L. Underwater image enhancement combining low-dimensional and global features. Vis Comput 39, 3029–3039 (2023). https://doi.org/10.1007/s00371-022-02510-5

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