DPLUT: Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors

Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. As a result, their practicality is limited. In this work, we devise a novel unsupervised LIE framework based on diffusion priors and lookup tables (DPLUT) to achieve efficient low-light image recovery. The proposed approach comprises two critical components: a light adjustment lookup table (LLUT) and a noise suppression lookup table (NLUT). LLUT is optimized with a set of unsupervised losses. It aims at predicting pixel-wise curve parameters for the dynamic range adjustment of a specific image. NLUT is designed to remove the amplified noise after the light brightens. As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in terms of visual quality and efficiency.

Authors

• Yunlong Lin Xiamen University
• Zhenqi Fu Tsinghua University, China
• Kairun Wen Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China School of Informatics,Xiamen University, China
• Tian Ye The Hong Kong University of Science and Technology (Guangzhou), China
• Sixiang Chen The Hong Kong University of Science and Technology (Guangzhou), China
• Ge Meng Xiamen University
• Yingying Wang
• Chui Kong Fudan University, China
• Yue Huang Xiamen University
• Xiaotong Tu Xiamen University
• Xinghao Ding Xiamen University

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