WV-LUT: Wide Vision Lookup Tables for Real-Time Low-Light Image Enhancement
Canlin Li, Haowen Su, Xin Tan, Xiangfei Zhang, Lizhuang Ma · IEEE Transactions on Multimedia · 2025
In recent years, the lookup tables (LUTs) with deep learning for image enhancement have achieved remarkable results with extremely high inference efficiency. However, when dealing with severely degraded low-light images, lookup-table-based methods tend to exhibit poor enhancement results due to the lack of contextual and global information. To address the limitations of current lookup-table-based methods in the low-light image enhancement task, we propose the novel Wide Vision Lookup Tables (WV-LUT) by introducing Complementary-Hierarchical 4D-LUTs into 3D-LUT, which allows 3D-LUT to have a wider range of vision. Specifically, the 4D-LUTs are used to expand the receptive field and process local information on a single channel, while a 3D-LUT is used for sRGB channel post-processing. Additionally, we propose a lightweight Global Adjustment Module that further enhances the performance and generalization of WV-LUT by obtaining global adjustment parameters for gamma and color correction matrix to adaptively process images. Experimental results demonstrate that our method outperforms other state-of-the-art methods in low-light image enhancement with the highest average ranking and superior inference efficiency. Furthermore, deployment experiments on mobile devices demonstrate that our WV-LUT achieves superior results and inference efficiency, showcasing promising application prospects for edge devices.