Abstract
Underwater images often suffer from severe degradation caused by light absorption and scattering, leading to color distortion, low contrast and reduced visibility. Existing Underwater Image Enhancement (UIE) methods can be divided into two categories, i.e., prior-based and learning-based methods. The former rely on rigid physical assumptions that limit the adaptability, while the latter often face data scarcity and weak generalization. To address these issues, we propose a Physics-Semantics-Guided Underwater Image Enhancement Network (PSG-UIENet), which couples the Retinex-grounded illumination correction with the language-informed guidance. This network comprises a Prior-Free Illumination Estimator, a Cross-Modal Text Aligner and a Semantics-Guided Image Restorer. In particular, the restorer leverages the textual descriptions generated by the Contrastive Language-Image Pre-training (CLIP) model to inject high-level semantics for perceptually meaningful guidance. Since multimodal UIE data sets are not publicly available, we also construct a large-scale image-text UIE data set, namely, LUIQD-TD, which contains 6,418 image-reference-text triplets. To explicitly measure and optimize semantic consistency between textual descriptions and images, we further design an Image-Text Semantic Similarity (ITSS) loss function. To our knowledge, this study makes the first effort to introduce both textual guidance and the multimodal data set into UIE tasks. Extensive experiments on our data set and four publicly available data sets demonstrate that the proposed PSG-UIENet achieves superior or comparable performance against fifteen state-of-the-art methods.
Abstract (translated)
水下图像往往因光吸收和散射而遭受严重退化，导致色彩失真、对比度低以及能见度降低。现有的水下图像增强（UIE）方法可以分为基于先验知识的方法和基于学习的方法两大类。前者依赖于刚性的物理假设，限制了其适应性；后者则通常面临数据稀缺及泛化能力弱的问题。为了解决这些问题，我们提出了一种由物理-语义引导的水下图像增强网络（PSG-UIENet），该网络结合了基于Retinex原理的光照校正与语言信息指导。这个网络包括一个无先验假设的光照估计器、一个多模态文本对齐器以及一种受语义指导的图像恢复器。特别是，恢复器利用由对比性语言图像预训练（CLIP）模型生成的文字描述来注入高层次的语义信息，提供感知上有意义的引导。由于多模态UIE数据集不公开可用，我们也构建了一个大规模的图文UIE数据集，称为LUIQD-TD，包含6,418个图像-参考-文本三元组。为了明确地测量和优化文字描述与图像之间的语义一致性，我们进一步设计了一种图像-文本语义相似性（ITSS）损失函数。据我们所知，这项研究首次将文本指导和多模态数据集引入到UIE任务中。在我们的数据集以及四个公开可用的数据集上进行的大量实验表明，提出的PSG-UIENet与现有的十五种最先进的方法相比，在性能上表现出优越或相当的表现。
URL
https://arxiv.org/abs/2603.07076
