A review of advancements in low-light image enhancement using deep learning

In low-light environments, the performance of computer vision algorithms often deteriorates significantly, adversely affecting key vision tasks such as segmentation, detection, and classification. With the rapid advancement of deep learning, its application to low-light image processing has attracted widespread attention and seen significant progress in recent years. However, there remains a lack of comprehensive surveys that systematically examine how recent deep-learning-based low-light image enhancement methods function and evaluate their effectiveness in enhancing downstream vison tasks. To address this gap, this review provides a detailed elaboration on how various recent approaches (from 2020) operate and their enhancement mechanisms, supplemented with clear illustrations. It also investigates the impact of different enhancement techniques on subsequent vision tasks, critically analyzing their strengths and limitations. Additionally, it proposes future research directions. This review serves as a useful reference for determining low-light image enhancement techniques and optimizing vision task performance in low-light conditions.

@article{liu2025_2505.05759,
  title={ A review of advancements in low-light image enhancement using deep learning },
  author={ Fangxue Liu and Lei Fan },
  journal={arXiv preprint arXiv:2505.05759},
  year={ 2025 }
}