  Underwater images often exhibit poor quality, distorted color balance and low
contrast due to the complex and intricate interplay of light, water, and
objects. Despite the significant contributions of previous underwater
enhancement techniques, there exist several problems that demand further
improvement: (i) The current deep learning methods rely on Convolutional Neural
Networks (CNNs) that lack the multi-scale enhancement, and global perception
field is also limited. (ii) The scarcity of paired real-world underwater
datasets poses a significant challenge, and the utilization of synthetic image
pairs could lead to overfitting. To address the aforementioned problems, this
paper introduces 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. Besides, we
introduce a special underwater semi-supervised training strategy, where we
propose 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.
