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<p>Vision serves as a crucial information source for underwater observation and operations; however, the quality of underwater imaging is often compromised by remarkable color distortion and detail loss, which are further exacerbated under nonuniform lighting conditions. The existing traditional nonlearning solutions often struggle to adapt to diverse underwater degradation, while purely data-driven learning strategies are often limited by scarce and low-quality samples, making it difficult to achieve satisfactory results. In contrast to existing joint learning frameworks, we propose a unified yet decoupled framework for effectively addressing the challenges of color correction and illumination enhancement in underwater images. Our proposed method employs distinct prediction and learning strategies to tackle these two key issues individually, thereby overcoming the limitations associated with the reference of learning samples that neglect lighting conditions. Consequently, the proposed approach yields enhanced overall visual effects for underwater image enhancement. Comparative experiments and ablation experiments on publicly available datasets have validated the effectiveness of the proposed self-attention-driven adaptive luminance transfer and multiple color space feature encoding. The source code and pretrained models are available on the project home page: <a href="https://github.com/OUCVisionGroup/MCAL-Net">https://github.com/OUCVisionGroup/MCAL-Net</a>.</p>

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MCAL-Net: multispace color compensation and adaptive luminance transfer network for underwater images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月14日

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  • Kunqian Li,
  • Wenjie Liu,
  • Zhou Ge,
  • Shuaixin Liu,
  • Dalei Song

摘要

Vision serves as a crucial information source for underwater observation and operations; however, the quality of underwater imaging is often compromised by remarkable color distortion and detail loss, which are further exacerbated under nonuniform lighting conditions. The existing traditional nonlearning solutions often struggle to adapt to diverse underwater degradation, while purely data-driven learning strategies are often limited by scarce and low-quality samples, making it difficult to achieve satisfactory results. In contrast to existing joint learning frameworks, we propose a unified yet decoupled framework for effectively addressing the challenges of color correction and illumination enhancement in underwater images. Our proposed method employs distinct prediction and learning strategies to tackle these two key issues individually, thereby overcoming the limitations associated with the reference of learning samples that neglect lighting conditions. Consequently, the proposed approach yields enhanced overall visual effects for underwater image enhancement. Comparative experiments and ablation experiments on publicly available datasets have validated the effectiveness of the proposed self-attention-driven adaptive luminance transfer and multiple color space feature encoding. The source code and pretrained models are available on the project home page: https://github.com/OUCVisionGroup/MCAL-Net.