SHLUT: Efficient Image Enhancement using Spatial‐Aware High‐Light Compensation Look‐up Tables

Abstract Recently, the look‐up table (LUT)‐based method has achieved remarkable success in image enhancement tasks with its high efficiency and lightweight nature. However, when considering edge scenarios with limited computational resources, most existing methods fail to meet practical requirements due to their costly floating‐point operations on convolution layers, which limit their general use. Moreover, most LUT‐based methods may not perform well in handling high‐light regions. To address these issues, we propose SHLUT, an efficient and practical image enhancement method by using spatial‐aware high‐light compensation look‐up tables (LUTs), which comprise two parts. Firstly, we propose a spatial‐aware weight predictor to reduce the computational burden. A lightweight network is trained to predict spatial‐aware weight values, and then we transfer the values to the LUTs. Additionally, to correct overexposure in high‐light regions, we propose a high‐light compensation 3D LUT. Our proposed method allows us to directly retrieve the values from the LUTs to achieve efficient image enhancement at test time. Extensive experimental results demonstrate that SHLUT exhibits competitive performance compared to other LUT‐based methods both quantitatively and qualitatively in a more efficient manner. For instance, SHLUT significantly reduces computational resources (at least 18 times in GFLOPs compared to other LUT‐based methods), while excelling in high‐light region handling.