WEDM: Wavelet-Enhanced Diffusion with Multi-Stage Frequency Learning for Underwater Image Enhancement
Year: 2025
Type: article
Abstract: Underwater image enhancement (UIE) is inherently challenging due to complex degradation effects such as light absorption and scattering, which result in color distortion and a loss of fine details. Most existing methods focus on spatial-domain processing, often neglecting the frequency-domain characteristics that are crucial for effectively restoring textures and edges. In this paper, we propose a novel UIE framework, the Wavelet-based Enhancement Diffusion Model (WEDM), which integrates frequency-domain decomposition with diffusion models. The WEDM consists of two main modules: the Wavelet Color Compensation Module (WCCM) for color correction in the LAB space using discrete wavelet transform, and the Wavelet Diffusion Module (WDM), which replaces traditional convolutions with wavelet-based operations to preserve multi-scale frequency features. By combining residual denoising diffusion with frequency-specific processing, the WEDM effectively reduces noise amplification and high-frequency blurring. Ablation studies further demonstrate the essential roles of the WCCM and WDM in improving color fidelity and texture details. Our framework offers a robust solution for underwater visual tasks, with promising applications in marine exploration and ecological monitoring.
Source: Journal of Imaging
Authors: Junhao Chen, Sichao Ye, Xiong Ouyang, Jiayan Zhuang
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4409279403