CDF-UIE: Leveraging Cross-Domain Fusion for Underwater Image Enhancement
Haopeng Zhang, Hongli Xu, Xiaosheng Yu, Xiangyue Zhang, Xiujing Gao, Chengdong Wu
Abstract
Underwater image enhancement (UIE) aims to restore image quality by mitigating inherent degradations in underwater imaging systems. While existing learning-based methods show promise, they face limitations in separating and processing frequency components, effectively fusing domain information, and balancing the enhancement of structures and details. To resolve these limitations, we propose cross-domain fusion (CDF)-UIE, a novel network that leverages and fuses cross-domain information for mitigating the degradation in underwater images. CDF-UIE first performs domain decoupling of input features using the proposed spatial-frequency decoupling (SFD) block. Then, we design an innovative CDF block, which effectively bridges the spatial- and frequency-domain features through the cross-domain attention mechanism. To produce stable and detailed enhanced outputs, we exploit the coarse and fine-scale information in the image reconstruction stage. In addition, we introduce a multiscale objective function that incorporates pixel-level, structural, and perceptual constraints to guide the enhancement process. We conduct extensive experiments on six diverse real-world underwater image datasets. Comprehensive experiments and real-world application tests demonstrate that CDF-UIE significantly outperforms existing methods, offering promising future applications in various underwater scenarios. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hpzhan66/CDF-UIE</uri>.