Abstract
Underwater image enhancement (UIE) techniques aim to improve visual quality of images captured in aquatic environments by addressing degradation issues caused by light absorption and scattering effects, including color distortion, blurring, and low contrast. Current mainstream solutions predominantly employ multi-scale feature extraction (MSFE) mechanisms to enhance reconstruction quality through multi-resolution feature fusion. However, our extensive experiments demonstrate that high-quality image reconstruction does not necessarily rely on multi-scale feature fusion. Contrary to popular belief, our experiments show that single-scale feature extraction alone can match or surpass the performance of multi-scale methods, significantly reducing complexity. To comprehensively explore single-scale feature potential in underwater enhancement, we propose an innovative Single-Scale Decomposition Network (SSD-Net). This architecture introduces an asymmetrical decomposition mechanism that disentangles input image into clean layer along with degradation layer. The former contains scene-intrinsic information and the latter encodes medium-induced interference. It uniquely combines CNN's local feature extraction capabilities with Transformer's global modeling strengths through two core modules: 1) Parallel Feature Decomposition Block (PFDB), implementing dual-branch feature space decoupling via efficient attention operations and adaptive sparse transformer; 2) Bidirectional Feature Communication Block (BFCB), enabling cross-layer residual interactions for complementary feature mining and fusion. This synergistic design preserves feature decomposition independence while establishing dynamic cross-layer information pathways, effectively enhancing degradation decoupling capacity.
Abstract (translated)
水下图像增强（UIE）技术旨在通过解决由光吸收和散射效应引起的颜色失真、模糊和低对比度等退化问题，来改善在水环境中拍摄的图像的视觉质量。当前主流解决方案主要采用多尺度特征提取（MSFE）机制，通过多分辨率特征融合提升重建质量。然而，我们经过广泛实验发现，高质量的图像重构并不一定依赖于多尺度特征融合。与普遍看法相反，我们的实验表明，单尺度特征提取本身可以匹配甚至超过多尺度方法的性能，并显著降低复杂度。 为了全面探索单尺度特征在水下增强中的潜力，我们提出了一种创新的单尺度分解网络（SSD-Net）。该架构引入了不对称的分解机制，将输入图像分解为干净层和退化层。前者包含场景固有的信息，而后者编码介质引起的干扰。通过两个核心模块的独特结合——CNN的局部特征提取能力和Transformer的整体建模优势，该架构实现了：1）并行特征解耦块（PFDB），通过高效的注意力操作和自适应稀疏变换实现双分支特征空间解耦；2）双向特征通信块（BFCB），支持跨层残差交互以进行互补特征挖掘与融合。这种协同设计在保持特征分解独立性的同时建立了动态的跨层信息路径，有效增强了退化分离能力。
URL
https://arxiv.org/abs/2508.04123
