低光图像和视频增强用于更强大的计算机视觉任务:一篇评论
Mpilo M Tatana1, Mohohlo S Tsoeu2, Rito C Maswanganyi1
1Department of Electronic and Computer Engineering, Durban University of Technology, Durban 4001, South Africa.
Journal of imaging
|April 25, 2025
概括
低光增强 (LLE) 对于计算机视觉任务至关重要. 深度学习模型的表现优于传统方法,但需要零射击学习者才能实现现实世界的稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 像对象检测和动作识别这样的计算机视觉任务受到恶劣的照明条件的阻碍.
- 低光增强 (LLE) 是一个未经探索但对于改善机器视觉感知至关重要的领域.
- 当前的计算机视觉系统在低光场景中通常需要额外的传感器.
研究的目的:
- 审查光增强 (LE) 的领域,特别是视频增强.
- 分析基于传统和深度学习的LE方法,并比较最近的模型.
- 调查LE对计算机视觉任务的性能和稳定性的影响.
主要方法:
- 基于传统和深度学习的光增强算法的比较分析.
- 评估最近的模型在低光增强.
- 评估光增强如何改善计算机视觉任务,如动作识别,物体检测和图像分类.
主要成果:
- 基于深度学习的增强器显著优于传统方法.
- 监督的深度学习模型取得了最佳结果,其次是零射击学习者.
- 整合光增强算法可以明显提高计算机视觉任务的性能和稳定性.
结论:
- 虽然受监督的学习者获得了最佳表现,但他们在现实世界的数据和稳定性方面的局限性需要向零射击学习方法转变.
- 增强光线对于在各种照明条件下提高计算机视觉能力至关重要.
- 未来的研究应该专注于开发更强大,更适应的零射击学习模型,以改善低光照明.
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