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Updated: Jan 17, 2026

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语义对比对域-强大的水下图像质量评估的语义对比.

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    此摘要是机器生成的。

    本研究介绍了SCUIA,一个无监督的水下图像质量评估 (UIQA) 框架. 它使用语义对比学习进行准确的质量预测,而不需要人类得分,改善了在各种水生环境中的概括性.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 图像处理 图像处理
    • 机器学习 机器学习

    背景情况:

    • 水下图像质量评估 (UIQA) 面临复杂的退化和域移动带来的挑战.
    • 现有的无参考IQA方法通常依赖于主观和昂贵的平均意见得分 (MOS),限制了它们的适用性.
    • 在没有人类注释的情况下将IQA模型推广到各种水生环境中仍然是一个重大障碍.

    研究的目的:

    • 提出SCUIA,一个不受监督的UIQA框架,可以在没有人类注释的情况下预测图像质量.
    • 利用语义对比学习来捕捉图像退化和质量之间的隐性相关性.
    • 增强模型概括性和域名适应性,以实现强大的UIQA.

    主要方法:

    • 引入了一种视觉语言对比学习策略,以在统一的语义空间中对齐图像和文本特征.
    • 开发了一种层次的对比学习机制,将统计先验和语义提示结合起来,以提高质量歧视.
    • 实施了一个使用本地统计特征的无监督域适应模块,以指导CLIP微调,以解开域不变质量表示.

    主要成果:

    • 在公开基准上,SCUIA比现有的UIQA方法取得了显著的改进.
    • 该框架在未见的领域中展示了卓越的概括能力.
    • 无监督的域调整使得有效的零射击跨域质量预测成为可能.

    结论:

    • SCUIA提供了一种强大而不受监督的方法来评估水下图像质量.
    • 提出的语义和层次对比学习策略有效地应对UIQA的挑战.
    • 该框架显示了对现实世界应用的巨大潜力,这些应用需要在各种水下条件下可靠的质量预测.