Abstract
Recently, learning-based Underwater Image Enhancement (UIE) methods have demonstrated promising performance. However, existing learning-based methods still face two challenges. 1) They rarely consider the inconsistent degradation levels in different spatial regions and spectral bands simultaneously. 2) They treat all regions equally, ignoring that the regions with high-frequency details are more difficult to reconstruct. To address these challenges, we propose a novel UIE method based on spatial-spectral dual-domain adaptive learning, termed SS-UIE. Specifically, we first introduce a spatial-wise Multi-scale Cycle Selective Scan (MCSS) module and a Spectral-Wise Self-Attention (SWSA) module, both with linear complexity, and combine them in parallel to form a basic Spatial-Spectral block (SS-block). Benefiting from the global receptive field of MCSS and SWSA, SS-block can effectively model the degradation levels of different spatial regions and spectral bands, thereby enabling degradation level-based dual-domain adaptive UIE. By stacking multiple SS-blocks, we build our SS-UIE network. Additionally, a Frequency-Wise Loss (FWL) is introduced to narrow the frequency-wise discrepancy and reinforce the model's attention on the regions with high-frequency details. Extensive experiments validate that the SS-UIE technique outperforms state-of-the-art UIE methods while requiring cheaper computational and memory costs.
Abstract (translated)
最近，基于学习的水下图像增强（UIE）方法展示了很有前景的表现。然而，现有的基于学习的方法仍然面临两个挑战：1) 它们很少同时考虑不同空间区域和光谱带之间不一致的退化程度；2) 它们对待所有区域同等处理，忽视了高频细节丰富的区域更难重建这一事实。为了解决这些挑战，我们提出了一种新的基于空间-光谱双域自适应学习的UIE方法，称为SS-UIE。 具体来说，我们首先引入了一个具有线性复杂度的空间级多尺度循环选择扫描（MCSS）模块和一个同样具备线性复杂度的光谱级自我注意（SWSA）模块，并将它们并行组合以形成基本的空间-光谱块（SS-block）。得益于MCSS和SWSA提供的全局感受野，SS-block能够有效地建模不同空间区域和光谱带上的退化程度，从而实现基于退化水平的双域自适应UIE。通过堆叠多个SS-block，我们构建了我们的SS-UIE网络。 此外，还引入了一种频率级损失（FWL），旨在缩小频率级别的差异，并强化模型对高频细节区域的关注。大量的实验验证表明，SS-UIE技术在性能上超越现有的最先进的UIE方法，同时所需的计算和内存成本更低。
URL
https://arxiv.org/abs/2504.19198
