USWformer: Efficient Sparse Wavelet Transformer for Underwater Image Enhancement

Transformer-based methods have shown great promise in underwater image enhancement (UIE) tasks due to their capability to model long-range dependencies, which are vital for reconstructing clear images. While numerous effective attention mechanisms have been devised to handle the computational requirements of transformers, they frequently incorporate redundant information and noisy interactions from irrelevant regions. Additionally, the current methods focusing solely on the raw pixel space constrains the exploration of the underwater image frequency dynamics, thus hindering the models from fully leveraging their potential for producing high-quality images. To address these challenges, we propose USWformer, an efficient UIE Sparse Wavelet Transformer Network (1.19 M parameters) to eliminate the redundant features in both the spatial and frequency domains. The USWformer consists of two fundamental components: a Sparse Wavelet Self-Attention (SWSA) block and a Multi-scale Wavelet Feed-Forward Network (MWFN). The SWSA block selectively preserves essential attention scores from the keys corresponding to each query, adjusting the feature details. MWFN further diminishes the feature redundancy in the aggregated features thereby improving the enhancement of the underwater images. We assess the efficacy of our approach across benchmark datasets comprising synthetic and real-world under-water images, showcasing its superiority via thorough ablation studies and comparative analyses.