Abstract
Underwater Image Enhancement (UIE) is an ill-posed problem where natural clean references are not available, and the degradation levels vary significantly across semantic regions. Existing UIE methods treat images with a single global model and ignore the inconsistent degradation of different scene components. This oversight leads to significant color distortions and loss of fine details in heterogeneous underwater scenes, especially where degradation varies significantly across different image regions. Therefore, we propose SUCode (Semantic-aware Underwater Codebook Network), which achieves adaptive UIE from semantic-aware discrete codebook representation. Compared with one-shot codebook-based methods, SUCode exploits semantic-aware, pixel-level codebook representation tailored to heterogeneous underwater degradation. A three-stage training paradigm is employed to represent raw underwater image features to avoid pseudo ground-truth contamination. Gated Channel Attention Module (GCAM) and Frequency-Aware Feature Fusion (FAFF) jointly integrate channel and frequency cues for faithful color restoration and texture recovery. Extensive experiments on multiple benchmarks demonstrate that SUCode achieves state-of-the-art performance, outperforming recent UIE methods on both reference and no-reference metrics. The code will be made public available at this https URL.
Abstract (translated)
水下图像增强（UIE）是一个不适定问题，因为自然干净的参考图无法获取，并且退化程度在语义区域间变化显著。现有的UIE方法采用单一全局模型处理图像，忽略了不同场景组件之间不一致的退化情况。这种忽视会导致在异构水下场景中出现明显的色彩扭曲和细节丢失，尤其是在图像的不同区域退化差异较大的情况下更为严重。因此，我们提出了SUCode（语义感知水下载入网络），它利用语义感知离散载入表示实现自适应UIE。与一次生成的载入方法不同，SUCode采用针对异构水下退化的像素级语义载入表示。 为了防止伪真实标签污染，在训练过程中采用了三阶段训练范式以表征原始水下图像特征。门控通道注意模块（GCAM）和频率感知特性融合（FAFF）共同集成了信道和频率线索，用于忠实的色彩恢复和纹理重建。在多个基准测试中的广泛实验表明，SUCode取得了最先进的性能，在参考和无参考指标上均优于最近的UIE方法。 代码将在以下网址公开发布：[此链接] (请根据实际情况提供具体URL)
URL
https://arxiv.org/abs/2602.10586
