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 lownormal 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 pixelwise 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.
The overall framework of our proposed DPLUT. In the training phase, DPLUT involves two main stages. (a) In the first stage, we learn a light adjustment lookup table (LLUT), which estimates pixel-wise curve parameters for yielding coarse normal-light images. (b) In the second stage, we learn a noise suppression lookup table (NLUT) by introducing knowledge of a diffusion model, aiming at removing the amplified noise and artifacts introduced from LLUT. In the testing phase, with the LLUT and NLUT, DPLUT can robustly recover perceptual-friendly results in real-time.
@article{lin2024unsupervised,
title={Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors},
author={Lin, Yunlong and Fu, Zhenqi and Wen, Kairun and Ye, Tian and Chen, Sixiang and Meng, Ge and Wang, Yingying and Huang, Yue and Tu, Xiaotong and Ding, Xinghao},
journal={arXiv preprint arXiv:2409.18899},
year={2024}
}