Abstract
Underwater image enhancement is vital for marine conservation, particularly coral reef monitoring. However, AI-based enhancement models often face dataset bias, high computational costs, and lack of transparency, leading to potential misinterpretations. This paper introduces EBA-AI, an ethics-guided bias-aware AI framework to address these challenges. EBA-AI leverages CLIP embeddings to detect and mitigate dataset bias, ensuring balanced representation across varied underwater environments. It also integrates adaptive processing to optimize energy efficiency, significantly reducing GPU usage while maintaining competitive enhancement quality. Experiments on LSUI400, Oceanex, and UIEB100 show that while PSNR drops by a controlled 1.0 dB, computational savings enable real-time feasibility for large-scale marine monitoring. Additionally, uncertainty estimation and explainability techniques enhance trust in AI-driven environmental decisions. Comparisons with CycleGAN, FunIEGAN, RAUNENet, WaterNet, UGAN, PUGAN, and UTUIE validate EBA-AI's effectiveness in balancing efficiency, fairness, and interpretability in underwater image processing. By addressing key limitations of AI-driven enhancement, this work contributes to sustainable, bias-aware, and computationally efficient marine conservation efforts. For interactive visualizations, animations, source code, and access to the preprint, visit: this https URL
Abstract (translated)
水下图像增强对于海洋保护，尤其是珊瑚礁监测至关重要。然而，基于AI的增强模型经常面临数据集偏差、高昂的计算成本和缺乏透明度的问题，这些问题可能导致误解。本文介绍了EBA-AI（伦理导向的认识偏差意识型人工智能框架），用于解决这些挑战。EBA-AI利用CLIP嵌入来检测和减轻数据集偏差，确保在各种水下环境中实现平衡表示。此外，它还集成了自适应处理以优化能源效率，在保持竞争力的增强质量的同时显著减少GPU使用量。在LSUI400、Oceanex和UIEB100上的实验表明，尽管峰值信噪比（PSNR）下降了可控的1.0分贝，但计算节省使得大规模海洋监测中的实时可行性成为可能。此外，不确定性估计和可解释性技术增强了对AI驱动环境决策的信任。与CycleGAN、FunIEGAN、RAUNENet、WaterNet、UGAN、PUGAN和UTUIE相比，EBA-AI在水下图像处理的效率、公平性和可解释性方面证明了其有效性。通过解决AI驱动增强的关键限制，这项工作为可持续、有意识偏差和计算高效的海洋保护努力做出了贡献。 对于交互式可视化、动画、源代码及预印本访问，请参见：[此链接](https://thislink.com)
URL
https://arxiv.org/abs/2507.15036
