DATDM: Dynamic attention transformer diffusion model for underwater image enhancement

文献类型： 外文期刊

第一作者： Hu, Wang

作者： Hu, Wang;Chen, Shitu;Zhang, Lijun;Zhang, Hongjin;Xu, Jingxiang;Luo, Tuyan;Zhang, Lijun;Zhang, Hongjin;Xu, Jingxiang;Liu, Zhixiang;Zhang, Shiwen

作者机构：

关键词： Diffusion model; Underwater image enhancement; Color correction; Dynamic attention transformer

期刊名称：ALEXANDRIA ENGINEERING JOURNAL （ 影响因子：6.8； 五年影响因子：6.1 ）

ISSN： 1110-0168

年卷期： 2025 年 126 卷

页码：

收录情况： SCI

摘要： Underwater image degradation presents complex challenges, significantly impairing the efficiency of underwater tasks. The mainstream underwater image enhancement (UIE) methods are roughly divided into traditional physics-based and data-driven deep-learning methods. However, physical model-based methods have poor generalization capabilities. Data-driven methods require high-quality training data and suffer from limited model stability. We propose a novel UIE framework, DATDM, to address the above limitations and improve degraded underwater images. We first introduce a color correction module (CCM) to strengthen the model's image restoration capabilities. On the other hand, we propose a novel dynamic attention transformer (DAT) denoising network with excellent performance. The proposed DAT denoising network utilizes a dynamic attention transformer mechanism that adaptively extracts feature information based on the complexity of the feature map, effectively capturing rich features while optimizing computational efficiency. The proposed DATDM method significantly outperforms the existing state-of-the-art methods, with peak signal-to-noise ratios of 24.05 and 27.39 and structural similarity index measures of 0.9233 and 0.9504 on the UIEB and LSUI datasets, respectively. The final experiments demonstrate that our DATDM achieves better performance and visual effects on UIE tasks.

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