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.
@article{arxiv.2507.02270,
title = {MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement},
author = {Fanghai Yi and Zehong Zheng and Zexiao Liang and Yihang Dong and Xiyang Fang and Wangyu Wu and Xuhang Chen},
journal= {arXiv preprint arXiv:2507.02270},
year = {2025}
}