信号处理2025，Vol.41Issue(2)：290-301,12.DOI:10.12466/xhcl.2025.02.008
基于质量感知域适应的水下图像增强质量评价
Quality-Aware Domain Adaptation for Underwater Image Enhancement Quality Assessment
摘要
Abstract
As the demand for high-quality underwater images continues to grow in the field of underwater research,un-derwater image enhancement(UIE)algorithms have been widely applied.To evaluate the quality of these enhanced un-derwater images,researchers have proposed several underwater image enhancement quality assessment(UIEQA)algo-rithms.However,UIEQA algorithms trained on known underwater scenes often face challenges when applied in un-known underwater scenes.Additionally,existing UIEQA algorithms typically rely on large amounts of annotated data,which are often difficult and resource-intensive to obtain.To address these issues,this paper proposes a quality-aware domain adaptation-based underwater image enhancement quality assessment(QaDA-UIEQA)algorithm.The proposed method includes quality assessment and quality-aware domain adaptation modules.First,the quality assessment module performs supervised quality assessment training on the source domain data to ensure the accuracy of the main task.Sec-ond,the quality-aware domain adaptation module,guided by textual information,used a cross-attention(CA)module to extract important quality characteristic information from visual feature information.Then,domain adaptation tech-niques were used to narrow the gap in quality characteristics between the source and target domains,thus enabling mod-els trained on known underwater scenes to effectively generalize to unknown underwater scenes.Experimental results on the SAUD+dataset showed that the proposed method achieves optimal results on four key performance metrics,com-pared with 13 other existing methods.Among them,the SRCC improved by 8.5%,compared with the second-best model.Additionally,ablation studies demonstrated that our proposed multimodal approach significantly enhances model performance.The proposed method not only exhibited excellent performance in UIEQA but also surpassed other com-parison methods in terms of prediction accuracy and generalization capability in a group maximum differentiation com-petition.Therefore,QaDA-UIEQA has stronger generalization and robustness,and it can maintain efficient and stable performance in complex real-world applications.关键词
水下图像增强/质量评价/域适应/多模态/卷积神经网络Key words
underwater image enhancement/quality assessment/domain adaptation/multimodal/convolutional neural networks分类
信息技术与安全科学引用本文复制引用
田芷欣,姜求平..基于质量感知域适应的水下图像增强质量评价[J].信号处理,2025,41(2):290-301,12.基金项目
国家自然科学基金(62271277)The National Natural Science Foundation of China(62271277) （62271277）
