Semi-Supervised Underwater Image Enhancement Method Using Multimodal Features and Dynamic Quality Repository

文献类型： 外文期刊

第一作者： Ding, Mu

作者： Ding, Mu;Li, Gen;Hu, Yu;Liu, Hangfei;Huang, Xiaohua;Ding, Mu;Hu, Qingsong

作者机构：

关键词： aquaculture; underwater image enhancement; multimodal contrastive learning; dynamic quality reliability repository

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.8； 五年影响因子：2.8 ）

ISSN：

年卷期： 2025 年 13 卷 6 期

页码：

收录情况： SCI

摘要： Obtaining clear underwater images is crucial for smart aquaculture, so it is necessary to repair degraded underwater images. Although underwater image restoration techniques have achieved remarkable results in recent years, the scarcity of labeled data poses a significant challenge to continued advancement. It is well known that semi-supervised learning can make use of unlabeled data. In this study, we proposed a semi-supervised underwater image enhancement method, MCR-UIE, which utilized multimodal contrastive learning and a dynamic quality reliability repository to leverage the unlabeled data during training. This approach used multimodal feature contrast regularization to prevent the overfitting of incorrect labels, and secondly, introduced a dynamic quality reliability repository to update the output as pseudo ground truth. The robustness and generalization of the model in pseudo-label generation and unlabeled data learning were improved. Extensive experiments conducted on the UIEB and LSUI datasets demonstrated that the proposed method consistently outperformed existing traditional and deep learning-based approaches in both quantitative and qualitative evaluations. Furthermore, its successful application to images captured from deep-sea cage aquaculture environments validated its practical value. These results indicated that MCR-UIE held strong potential for real-world deployment in intelligent monitoring and visual perception tasks in complex underwater scenarios.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序