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.
LoopLUT: 3D Lookup Tables with Progressive Region Refinement for Real-Time 4K Image Enhancement
Universiti Sains Malaysia（马来西亚理科大学）；National University of Defense Technology（国防科技大学）；Sun Yat-sen University（中山大学）；Shandong Normal University（山东师范大学）；Shandong University of Finance and Economics（山东财经大学）
UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Robotics, Wuhan University（武汉大学机器人学院）；School of Automation, Southeast University（东南大学自动化学院）
Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables
IPAI, Seoul National University, Seoul, Korea（IPAI，首尔国立大学，首尔，韩国）；LG Electronics, Seoul, Korea（LG电子，首尔，韩国）；Department of ECE, INMC, Seoul National University, Seoul, Korea（电子工程系，INMC，首尔国立大学，首尔，韩国）
AttentionLut: Attention Fusion-based Canonical Polyadic LUT for Real-time Image Enhancement
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（上海交通大学图像通信与网络工程研究所）；Transsion（传音控股）；John Hopcroft Center, Shanghai Jiao Tong University（上海交通大学约翰·霍普克罗夫特中心）
LL-ViT: Edge Deployable Vision Transformers with Look Up Table Neurons
Department of Electrical and Computer Engineering, The University of Texas at Austin（电气与计算机工程系，德克萨斯大学奥斯汀分校）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Federal University of Rio de Janeiro（里约热内卢联邦大学）；Instituto de Telecomunicações（电信研究所）
