Underwater Image Enhancement via Advantage Feature Weighted Fusion

Weidong Zhang, Muzi Wang, Peixian Zhuang, Dahai Liu

Abstract: Light propagation underwater is susceptible to wavelength attenuation and scattering, leading to degradation plagued by color distortion, contrast degradation, and reduced visibility in underwater imaging. To handle the degradations, the paper proposes an underwater image enhancement method via advantage feature weighted fusion, called AFWF. Specifically, we propose a three-channel contrast enhancement strategy that effectively reduces the color distortion of a raw input image via a three-channel adaptive color compensation strategy. Meanwhile, we employ a fast exposure fusion to integrate the image sequences obtained from the multi-scale gamma correction and adaptive contrast enhancement strategies to improve the global contrast of the above-mentioned image. Subsequently, a single-channel contrast enhancement is proposed to improve the local contrast and edge detail information by enhancing the multi-level details of the raw image. Finally, we adopt the advantage feature weighted fusion strategy to analyze and selectively fuse the advantage feature of different enhanced images layer by layer to reconstruct a high-quality result. Extensive experimental verification results highlight that our AFWF method is superior to the state-of-the-art (SOTA) methods in improving raw underwater images’ color, contrast, and detail. The code is publicly available at: https://www.researchgate.net/publication/393021384_2025-AFWF.

External IDs:doi:10.1109/tcsvt.2025.3603059