UWMambaNet: Dual-Branch Underwater Image Reconstruction Based on W-Shaped Mamba

Yuhan Zhang, Xinyang Yu and Zhanchuan Cai ()
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Yuhan Zhang: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
Xinyang Yu: School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China
Zhanchuan Cai: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China

Mathematics, 2025, vol. 13, issue 13, 1-18

Abstract: Underwater image enhancement is a challenging task due to the unique optical properties of water, which often lead to color distortion, low contrast, and detail loss. At the present stage, the methods based on the CNN have the problem of insufficient global attention, and the methods based on Transformer generally have the problem of quadratic complexity. To address this challenge, we propose a dual-branch network architecture based on the W-shaped Mamba: UWMambaNet. Our method integrates the color contrast enhancement branch and the detail enhancement branch, and each branch is dedicated to improving specific aspects of underwater images. The color contrast enhancement branch utilizes the RGB and Lab color spaces and uses the Mamba block for advanced feature fusion to enhance color fidelity and contrast. The detail enhancement branch adopts a multi-scale feature extraction strategy to capture fine and contextual details through parallel convolutional paths. The Mamba module is added to the dual branches, and state-space modeling is used to capture the long-range dependencies and spatial relationships in the image data. This enables effective modeling of the complex interactions and light propagation effects inherent in the underwater environment. Experimental results show that our method significantly improves the visual quality of underwater images and is superior to existing technologies in terms of quantitative indicators and visualization effects; compared to the best candidate models on the UIEB and EUVP datasets, UWMambaNet improves UCIQE by 3.7% and 2.4%, respectively.

Keywords: underwater image enhancement; dual-path Mamba network; global detail enhancement; state-space model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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