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Global structure-guided learning framework for underwater image enhancement

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Abstract

Underwater image enhancement (UIE), as an image processing technique, plays a vital role in computer vision. However, existing approaches treat the restoration process as a whole; thus, they cannot adequately handle the color distortion and low contrast in the enhanced images. In this paper, we propose a global–local-guided model for realizing UIE tasks in a coarse-to-fine manner to alleviate these issues. The proposed model is divided into two paths. The global path targets to estimate basic structure and color information, while the local path targets to remove the undesirable artifacts, e.g., noises over-exposure regions, and blurred edges. By integrating two neural networks into our model, we could recover the underwater images with clear textural details and vivid color. Besides, a learning-based weight map is introduced to make the global–local path on friendly terms, which can balance the pixel intensity distribution from both sides and remove redundant information to a certain degree. Qualitative and quantitative experimental results on various benchmarks demonstrate that our method can effectively tackle color distortion and blurred edges compared with several state-of-the-art methods by a large margin. Finally, we also conduct experiments to demonstrate that our method can be applied in various computer vision tasks, e.g., object detection, matching and edge detection.

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Funding

The funding was provided by National Natural Science Foundation of China (Grant Nos. 61922019, 62027826, 61722105, 61672125).

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Authors and Affiliations

  1. School of Software Technology, Dalian University of Technology, Dalian, China

    Jinyuan Liu

  2. International School of Information Science and Engineering, Dalian University of Technology, Dalian, China

    Runjia Lin, Risheng Liu & Xin Fan

  3. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China

    Jinyuan Liu, Risheng Liu & Xin Fan

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  1. Runjia Lin
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  4. Xin Fan
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Correspondence to Risheng Liu.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Global Structure-Guided Learning Framework for Underwater Image Enhancement.”

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Lin, R., Liu, J., Liu, R. et al. Global structure-guided learning framework for underwater image enhancement. Vis Comput 38, 4419–4434 (2022). https://doi.org/10.1007/s00371-021-02305-0

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