MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915370185523200 |
|---|---|
| author | Yi, Fanghai Zheng, Zehong Liang, Zexiao Dong, Yihang Fang, Xiyang Wu, Wangyu Chen, Xuhang |
| author_facet | Yi, Fanghai Zheng, Zehong Liang, Zexiao Dong, Yihang Fang, Xiyang Wu, Wangyu Chen, Xuhang |
| contents | Enhancing underwater images is crucial for exploration. These images face visibility and color issues due to light changes, water turbidity, and bubbles. Traditional prior-based methods and pixel-based methods often fail, while deep learning lacks sufficient high-quality datasets. We introduce the Multi-Axis Conditional Lookup (MAC-Lookup) model, which enhances visual quality by improving color accuracy, sharpness, and contrast. It includes Conditional 3D Lookup Table Color Correction (CLTCC) for preliminary color and quality correction and Multi-Axis Adaptive Enhancement (MAAE) for detail refinement. This model prevents over-enhancement and saturation while handling underwater challenges. Extensive experiments show that MAC-Lookup excels in enhancing underwater images by restoring details and colors better than existing methods. The code is https://github.com/onlycatdoraemon/MAC-Lookup. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02270 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement Yi, Fanghai Zheng, Zehong Liang, Zexiao Dong, Yihang Fang, Xiyang Wu, Wangyu Chen, Xuhang Computer Vision and Pattern Recognition Enhancing underwater images is crucial for exploration. These images face visibility and color issues due to light changes, water turbidity, and bubbles. Traditional prior-based methods and pixel-based methods often fail, while deep learning lacks sufficient high-quality datasets. We introduce the Multi-Axis Conditional Lookup (MAC-Lookup) model, which enhances visual quality by improving color accuracy, sharpness, and contrast. It includes Conditional 3D Lookup Table Color Correction (CLTCC) for preliminary color and quality correction and Multi-Axis Adaptive Enhancement (MAAE) for detail refinement. This model prevents over-enhancement and saturation while handling underwater challenges. Extensive experiments show that MAC-Lookup excels in enhancing underwater images by restoring details and colors better than existing methods. The code is https://github.com/onlycatdoraemon/MAC-Lookup. |
| title | MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.02270 |