Innovative collaborative multi-lookup table for real-time enhancement of low-light images

This paper proposes CML-Net, a novel collaborative multi-lookup table network, tailored for real-time enhancement of severely degraded low-light images. By introducing a cascade of 1D and 4D lookup tables within a single channel, CML-Net expands the receptive field and enhances the ability to process local pixel information. A lightweight global enhancement module utilizing parallel Vision State-Space Modules is designed for fast global information extraction, providing adaptive gamma and color correction parameters. Experimental results demonstrate that CML-Net outperforms state-of-the-art methods, achieving an average rank of 2.2 and 1.8 on full-reference and non-reference datasets, respectively, while maintaining real-time processing capabilities. Deployment tests on mobile devices showcase its potential for edge device applications.