Abstract
We present LoR-LUT, a unified low-rank formulation for compact and interpretable 3D lookup table (LUT) generation. Unlike conventional 3D-LUT-based techniques that rely on fusion of basis LUTs, which are usually dense tensors, our unified approach extends the current framework by jointly using residual corrections, which are in fact low-rank tensors, together with a set of basis LUTs. The approach described here improves the existing perceptual quality of an image, which is primarily due to the technique's novel use of residual corrections. At the same time, we achieve the same level of trilinear interpolation complexity, using a significantly smaller number of network, residual corrections, and LUT parameters. The experimental results obtained from LoR-LUT, which is trained on the MIT-Adobe FiveK dataset, reproduce expert-level retouching characteristics with high perceptual fidelity and a sub-megabyte model size. Furthermore, we introduce an interactive visualization tool, termed LoR-LUT Viewer, which transforms an input image into the LUT-adjusted output image, via a number of slidebars that control different parameters. The tool provides an effective way to enhance interpretability and user confidence in the visual results. Overall, our proposed formulation offers a compact, interpretable, and efficient direction for future LUT-based image enhancement and style transfer.
Abstract (translated)
我们提出了LoR-LUT，这是一种用于生成紧凑且可解释的3D查找表（LUT）的统一低秩表示方法。与传统的基于3D-LUT的方法不同，后者依赖于基LUT的融合，而这些基LUT通常是密集张量，我们的统一方法通过同时使用残差校正来扩展当前框架，这些残差实际上是低秩张量，并与一组基LUT结合使用。这里描述的方法主要由于其对残差校正的新颖应用，提高了图像现有的感知质量。与此同时，我们利用了显著较少的网络、残差校正和LUT参数，在保持相同的三线性插值复杂度的情况下实现了这一目标。 在MIT-Adobe FiveK数据集上训练得到的LoR-LUT模型能够以不到1MB的大小高度逼真地复现专家级图像修饰特性，具有良好的感知保真度。此外，我们还引入了一个交互式可视化工具，名为LoR-LUT Viewer，通过调整多个滑块来控制不同参数，该工具可以将输入图片转换为经过LUT校准后的输出图片。此工具提供了一种有效的方法以增强视觉结果的可解释性和用户信心。 总体而言，我们的方法提供了未来基于LUT的图像增强和风格迁移的一种紧凑、可解释且高效的途径。
URL
https://arxiv.org/abs/2602.22607
