Abstract
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.
Abstract (translated)
基于三维查找表（3D LUT）的图像增强方法通过在顶点处插值预先计算的值，能够有效地减少模型大小和运行时间。然而，由于3D LUT方法缺乏空间信息，在处理颜色值时是逐点进行转换的，因此存在局限性。尽管具有空间感知能力的3D LUT方法解决了这一限制问题，但它们引入了需要大量参数的额外模块，并且随着图像分辨率的增加会导致运行时间显著延长。 为了解决这个问题，我们提出了一种生成适应于图像的LUT的方法，该方法专注于查找表中的冗余部分。我们的高效框架将一个3D LUT分解成低维LUT的线性组合，并使用奇异值分解（SVD）。此外，我们还增强了用于空间特征融合的模块以提高缓存效率。 大量的实验结果表明，我们的模型在减少参数数量和运行时间的同时，还能保持对空间信息的关注以及性能水平。
URL
https://arxiv.org/abs/2508.16121
