ControlLight: Towards Controllable, Consistent, and Generalizable Low-Light Enhancement

ArXi:2605.25569v2 Announce Type: replace 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.