Frequency-Driven Diffusion: A Hierarchical Attention Weighting Framework for Underwater Image Restoration

Abstract

Underwater images often suffer from visual degradation, affecting downstream tasks. While recent underwater image enhancement (UIE) techniques have made some advances benefiting from deep neural networks, challenges remain in restoring fine details and achieving computational efficiency. Inspired by the success of diffusion models in image generation, we propose the Underwater Laplacian-Guided Diffusion Model (ULDM), which enhances image features layer-by-layer based on the hierarchical structure of the Laplacian pyramid transform to achieve both high-quality and efficient UIE. The Laplacian pyramid decomposes the degraded image into high- and low-frequency components, enabling the model to denoise the low-frequency spectrum and address global image degradation, thereby reducing computational overhead. To efficiently enhance high-frequency details, we introduce the Hierarchical Attention Weighted Module (HAWM) that leverages the strong pixel correlations in high-frequency sub-images at different levels, adjusting them layer-by-layer to better capture fine details. These high-frequency sub-images exhibit strong pixel correlation and consistent texture features across different layers, and their hierarchical pattern ensures effective detail restoration. Extensive experiments demonstrate that ULDM outperforms state-of-the-art methods in both quantitative and qualitative evaluations.