UWMambaNet: Dual-Branch Underwater Image Reconstruction Based on W-Shaped Mamba
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 Transf...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/13/2153 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2227-7390 |