基于多轴条件查找的水下图像增强模型，提升色彩与细节清晰度。
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
- 通过条件3D查表实现颜色初步校正，多轴自适应增强细化细节
- 在多个水下数据集上优于现有方法，显著恢复色彩与纹理细节
- 适合水下摄影、海洋探测等场景，对过增强有良好抑制能力
水下图像增强对探索至关重要。由于光线变化、水体浑浊和气泡影响，水下图像普遍存在可见度低和色彩失真问题。传统基于先验的方法和像素级方法常失效，而深度学习受限于高质量数据集不足。本文提出多轴条件查找（MAC-Lookup）模型，通过条件3D查找表颜色校正（CLTCC）进行初步色彩与质量修正，并结合多轴自适应增强（MAAE）实现细节精细化处理。该模型能有效防止过增强与饱和现象，应对水下复杂环境挑战。大量实验表明，相比现有方法，MAC-Lookup在恢复图像细节与色彩方面表现更优。代码已公开：https://github.com/onlycatdoraemon/MAC-Lookup。
原文摘要 · Abstract (English)
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.
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