Abstract
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 this https URL.
Abstract (translated)
改善水下图像对于探索至关重要。这些图像由于光线变化、水质浑浊和气泡的影响，往往会面临能见度低和色彩失真的问题。传统的基于先验的方法和像素级方法往往效果不佳，而深度学习又缺乏足够高质量的数据集。我们引入了多轴条件查找（MAC-Lookup）模型，该模型通过提高颜色准确性、清晰度和对比度来提升视觉质量。它包括用于初步颜色和质量校正的条件3D查找表色彩校正（CLTCC），以及用于细节微调的多轴自适应增强（MAAE）。此模型能够防止过度增强和饱和，并且可以有效处理水下环境中的各种挑战。广泛的实验表明，MAC-Lookup在恢复图像细节和颜色方面优于现有的方法，在提升水下图像质量上表现出色。该代码位于此链接：[https URL]。
URL
https://arxiv.org/abs/2507.02270
