基于深度信息的YCbCr查表法，实现实时水下图像增强。
DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement
- 利用深度信息动态调整YCbCr空间的查表参数，实现自适应增强。
- 在UIEB-90和LSUI数据集上表现优秀，4K图像处理速度达7毫秒。
- 适合部署于资源受限设备，兼顾效率与物理合理性。
水下图像增强面临空间非均匀、波长依赖性衰减的挑战，其退化程度由传播距离和波长决定。YCbCr色彩空间将亮度与色度分离，有利于恢复。本文提出DY-LUT：一种深度感知的YCbCr查表框架，用于实时增强。双分支编码器从图像和深度特征中联合预测图像级融合权重与像素级退化指数。这些量作为条件输入可学习的4维查表（LUT），后续辅以轻量级局部优化。该方法保持传统LUT高效性的同时，实现深度条件驱动的空间自适应恢复。在外部提供深度信息的情况下，356万参数的增强网络在UIEB-90和LSUI数据集上达到竞争力性能，运行速度比主流高容量基线快9至304倍。自适应推理进一步确保4K UIQAD图像处理时间约7毫秒。实验表明，相比RGB，YCbCr更适合作为深度条件查表的基础；联合学习的退化指数显著提升自适应查询效果。结果为在实际平台实现高效水下图像增强提供了物理合理的路径。
原文摘要 · Abstract (English)
Underwater image enhancement is challenged by spatially non-uniform, wavelength-dependent attenuation. Propagation distance and wavelength govern this degradation, while YCbCr separates luminance from chrominance for restoration. We propose DY-LUT, a depth-aware YCbCr lookup-table framework for real-time enhancement. A dual-branch encoder predicts image-level fusion weights and a joint pair of pixel-wise degradation indices from image and depth features. These quantities condition learnable 4D LUTs, followed by lightweight local refinement. DY-LUT preserves traditional LUT efficiency while enabling depth-conditioned, spatially adaptive restoration. With externally supplied depth, its 3.56M-parameter enhancement network achieves competitive quality on UIEB-90 and LSUI and runs $9$--$304\times$ faster than representative high-capacity baselines. Adaptive inference further maintains real-time performance ($\sim7$ ms) for 4K UIQAD images. DY-LUT also benefits downstream detection and feature matching. Ablations show that YCbCr is a more effective basis than RGB for depth-conditioned lookup, while the jointly learned indices further improve adaptive querying. These results provide a physically grounded route to efficient UIE on practical platforms.
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