Scholarlink

<p>Underwater image enhancement is critical for marine exploration and robotic vision. However, real-world underwater images suffer from color distortion, low contrast, and detail loss, while existing deep learning methods are often computationally heavy. This work presents a lightweight hybrid-domain enhancement network that jointly models frequency and spatial information for effective underwater image restoration. The architecture integrates frequency-spatial attention, channel attention pointwise, and multi-shape convolution to balance performance and efficiency. Experiments on public datasets show that the proposed method achieves higher PSNR and SSIM than mainstream methods with lower computational cost, supporting real-time deployment on resource-constrained devices. This work provides an effective solution for high-quality and efficient underwater vision applications. The source code and implementation details are publicly available at <a href="https://github.com/cfengsun16/HDAMS-Net">https://github.com/cfengsun16/HDAMS-Net</a> to ensure reproducibility.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hybrid-domain attention learning for lightweight underwater image enhancement and real-time vision deployment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

查看全文
  • Shan Jiang,
  • Chenfeng Sun,
  • Xuan Liu

摘要

Underwater image enhancement is critical for marine exploration and robotic vision. However, real-world underwater images suffer from color distortion, low contrast, and detail loss, while existing deep learning methods are often computationally heavy. This work presents a lightweight hybrid-domain enhancement network that jointly models frequency and spatial information for effective underwater image restoration. The architecture integrates frequency-spatial attention, channel attention pointwise, and multi-shape convolution to balance performance and efficiency. Experiments on public datasets show that the proposed method achieves higher PSNR and SSIM than mainstream methods with lower computational cost, supporting real-time deployment on resource-constrained devices. This work provides an effective solution for high-quality and efficient underwater vision applications. The source code and implementation details are publicly available at https://github.com/cfengsun16/HDAMS-Net to ensure reproducibility.