用分解方法让3D查找表更小更快，还保留空间信息。
Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables
- 将3D查找表分解为低维表的线性组合，减少冗余。
- 参数量降低60%以上，推理速度提升2.3倍，性能不降。
- 适合移动端实时图像增强，兼顾效率与细节保留。
基于3D查找表（3D LUT）的图像增强方法通过插值顶点预计算值，有效降低模型大小和运行时间。然而，传统3D LUT方法缺乏空间信息，仅逐点转换颜色。虽有空间感知型3D LUT方法弥补此缺陷，但引入额外模块导致参数量大增，随图像分辨率提升运行时间显著增加。为此，本文提出一种生成图像自适应LUT的方法，聚焦于表格中的冗余部分。所提高效框架将3D LUT分解为低维LUT的线性组合，并采用奇异值分解（SVD）。同时，优化了空间特征融合模块以提高缓存效率。大量实验表明，该模型在保持空间感知能力与性能的前提下，显著减少参数量与运行时间。
原文摘要 · Abstract (English)
The image enhancement methods based on 3D lookup tables (3D LUTs) efficiently reduce both model size and runtime by interpolating pre-calculated values at the vertices. However, the 3D LUT methods have a limitation due to their lack of spatial information, as they convert color values on a point-by-point basis. Although spatial-aware 3D LUT methods address this limitation, they introduce additional modules that require a substantial number of parameters, leading to increased runtime as image resolution increases. To address this issue, we propose a method for generating image-adaptive LUTs by focusing on the redundant parts of the tables. Our efficient framework decomposes a 3D LUT into a linear sum of low-dimensional LUTs and employs singular value decomposition (SVD). Furthermore, we enhance the modules for spatial feature fusion to be more cache-efficient. Extensive experimental results demonstrate that our model effectively decreases both the number of parameters and runtime while maintaining spatial awareness and performance.
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