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Attention-based color consistency underwater image enhancement network

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Abstract

Underwater images often exhibit color deviation, reduced contrast, distortion, and other issues due to light refraction, scattering, and absorption. Therefore, restoring detailed information in underwater images and obtaining high-quality results are primary objectives in underwater image enhancement tasks. Recently, deep learning-based methods have shown promising results, but handling details in low-light underwater image processing remains challenging. In this paper, we propose an attention-based color consistency underwater image enhancement network. The method consists of three components: illumination detail network, balance stretch module, and prediction learning module. The illumination detail network is responsible for generating the texture structure and detail information of the image. We introduce a novel color restoration module to better match color and content feature information, maintaining color consistency. The balance stretch module compensates using pixel mean and maximum values, adaptively adjusting color distribution. Finally, the prediction learning module facilitates context feature interaction to obtain a reliable and effective underwater enhancement model. Experiments conducted on three real underwater datasets demonstrate that our approach produces more natural enhanced images, performing well compared to state-of-the-art methods.

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Funding

This research was supported by the National Natural Science Foundation of China (61772319, 62002200, 62202268, 61972235).

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Authors and Affiliations

  1. School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264005, China

    Baocai Chang, Jinjiang Li & Mengjun Li

  2. Institute of Network Technology, Yantai, 264005, China

    Haiyang Wang

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  1. Baocai Chang
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  2. Jinjiang Li
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  4. Mengjun Li
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Contributions

Baocai Chang: Conceptualization, Methodology, Software. Jinjiang Li: Formal analysis, Methodology, Validation. Haiyang Wang: Data curation, Writing - original draft. Mengjun LI: Supervision, Visualization, Resources.

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Correspondence to Mengjun Li.

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Chang, B., Li, J., Wang, H. et al. Attention-based color consistency underwater image enhancement network. SIViP 18, 4385–4394 (2024). https://doi.org/10.1007/s11760-024-03080-w

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  • DOI: https://doi.org/10.1007/s11760-024-03080-w

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