针对水下图像增强中处理过与不足的问题，提出自适应分风格增强框架。
Style-Decoupled Adaptive Routing Network for Underwater Image Enhancement
- 将图像退化风格与结构分离，动态生成风格嵌入
- 在真实数据集上达到25.72 dB的PSNR新高
- 适合需要精准修复不同退化程度水下图像的场景
水下图像增强对海洋视觉感知至关重要。现有方法多依赖平均分布的统一映射，导致轻微退化图像过度处理、严重退化图像恢复不足。为此，我们提出新型自适应增强框架SDAR-Net。不同于传统统一范式，该方法首先从输入中解耦特定退化风格，并自适应调节增强过程。由于水下退化主要改变外观而保持场景结构，SDAR-Net通过精心设计的训练框架，将图像特征分解为动态退化风格嵌入和静态场景结构表征。随后引入自适应路由机制：通过评估风格特征并自适应预测不同增强状态下的软权重，引导对应图像表征的加权融合，精确满足每张图像的自适应恢复需求。大量实验表明，SDAR-Net在真实世界基准上取得25.72 dB的PSNR新纪录，并在下游视觉任务中展现实用性。代码已开源。
原文摘要 · Abstract (English)
Underwater Image Enhancement (UIE) is essential for robust visual perception in marine applications. However, existing methods predominantly rely on uniform mapping tailored to average dataset distributions, leading to over-processing mildly degraded images or insufficient recovery for severe ones. To address this challenge, we propose a novel adaptive enhancement framework, SDAR-Net. Unlike existing uniform paradigms, it first decouples specific degradation styles from the input and subsequently modulates the enhancement process adaptively. Specifically, since underwater degradation primarily shifts the appearance while keeping the scene structure, SDAR-Net formulates image features into dynamic degradation style embeddings and static scene structural representations through a carefully designed training framework. Subsequently, we introduce an adaptive routing mechanism. By evaluating style features and adaptively predicting soft weights at different enhancement states, it guides the weighted fusion of the corresponding image representations, accurately satisfying the adaptive restoration demands of each image. Extensive experiments show that SDAR-Net achieves a new state-of-the-art (SOTA) performance with a PSNR of 25.72 dB on real-world benchmark, and demonstrates its utility in downstream vision tasks. Our code is available at https://github.com/WHU-USI3DV/SDAR-Net.
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