MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
This work addresses underwater image enhancement for exploration applications, presenting an incremental improvement over prior deep learning approaches.
The paper tackles the problem of enhancing underwater images, which suffer from visibility and color issues, by introducing the MAC-Lookup model that improves color accuracy, sharpness, and contrast, with experiments showing it restores details and colors better than existing methods.
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.