| title | ControlLight: Towards Controllable, Consistent, and Generalizable Low-Light Enhancement |
|---|---|
| authors | Yufeng Yang, Jianzhuang Liu, Jisheng Chu, Yuqi Peng, Xianfang Zeng, Jiancheng Huang, Shifeng Chen |
| date | 2026-05-25 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.25569 |
| upvotes | 19 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Existing deep learning-based low-light enhancement methods are typically trained on limited datasets with single enhancement targets, which restricts their generalization ability and controllability in real-world applications. To overcome these limitations, we propose ControlLight, a controllable, consistent, and generalizable framework for low-light enhancement. We first construct a large-scale dataset of real-world degraded images with continuous illumination-strength supervision. To further ensure consistent outputs under different control strengths, we introduce a misalignment-aware weighted flow matching loss that preserves image structure across continuous enhancement strengths. ControlLight allows users to edit real-world degraded low-light images toward satisfactory enhancement results by flexibly controlling the strength while preserving visual consistency and realism. Extensive experiments show that ControlLight achieves state-of-the-art performance against existing low-light enhancement approaches while demonstrating strong continuous controllability and generalization to real-world scenarios. |
cite
@misc{yang2026controllight,
title={ControlLight: Towards Controllable, Consistent, and Generalizable Low-Light Enhancement},
author={Yufeng Yang and Jianzhuang Liu and Jisheng Chu and Yuqi Peng and Xianfang Zeng and Jiancheng Huang and Shifeng Chen},
year={2026},
eprint={2605.25569},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.25569}
}