Abstract
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.
Abstract (translated)
在低光环境下，计算机视觉算法的性能往往显著下降，严重影响了分割、检测和分类等关键任务的表现。随着深度学习的迅速发展，其在低光图像处理领域的应用吸引了广泛的关注，并在过去几年中取得了重大进展。然而，目前仍缺乏全面且系统地评估最近基于深度学习的低光图像增强方法的研究综述，这些方法的功能及其对下游视觉任务效果的影响尚未得到充分探讨。为填补这一空白，本文详细阐述了2020年以来各种最新方法的工作原理及它们的增强机制，并附有清晰的图示说明。此外，本文还研究了不同增强技术对后续视觉任务的影响，对其优缺点进行了批判性分析，并提出了未来的研究方向。这篇综述对于确定低光图像增强技术以及在低光条件下优化视觉任务性能具有重要的参考价值。
URL
https://arxiv.org/abs/2505.05759
