Underwater images often exhibit poor quality, imbalanced coloration, and low
contrast due to the complex and intricate interaction of light, water, and
objects. Despite the significant contributions of previous underwater
enhancement techniques, there exist several problems that demand further
improvement: (i) Current deep learning methodologies depend on Convolutional
Neural Networks (CNNs) that lack multi-scale enhancement and also have limited
global perception fields. (ii) The scarcity of paired real-world underwater
datasets poses a considerable challenge, and the utilization of synthetic image
pairs risks overfitting. To address the aforementioned issues, this paper
presents a Multi-scale Transformer-based Network called UWFormer for enhancing
images at multiple frequencies via semi-supervised learning, in which we
propose a Nonlinear Frequency-aware Attention mechanism and a Multi-Scale
Fusion Feed-forward Network for low-frequency enhancement. Additionally, we
introduce a specialized underwater semi-supervised training strategy, proposing
a Subaqueous Perceptual Loss function to generate reliable pseudo labels.
Experiments using full-reference and non-reference underwater benchmarks
demonstrate that our method outperforms state-of-the-art methods in terms of
both quantity and visual quality