当低光遇到闪光灯时:朝着同步的闪光灯移除和亮度增强
Jiahuan Ren1, Zhao Zhang2, Suiyi Zhao2
1Huaiyin Institute of Technology, Huaian, 223003, China; Hefei University of Technology, Hefei, 230601, China; The Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei, 230601, China.
概括
本研究介绍了Flared Low-Light Image Enhancement (FLLIE) 技术,以应对在低光和闪光灯的情况下提高图像可见性的挑战. 一个新的同步闪光灯移除和亮度增强 (SFRBE) 网络有效地增强了这些困难的图像.
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:
- 低光图像增强 (LLIE) 旨在提高可见性,但在现实世界的图像中扎着火焰.
- 现有的方法面临着诸如不均的照明,噪音放大和黑暗地区的细节损失等挑战.
研究的目的:
- 提出一个新的任务:燃烧的低光图像增强 (FLLIE).
- 为FLLIE开发一个有效的深度学习网络.
主要方法:
- 合成燃烧的低光数据集用于训练和推断.
- 开发了一个基于福里埃变换的深度网络:同步闪光灯移除和亮度增强 (SFRBE).
- 引入了用于频域特征提取的剩余定向里埃块 (RDFB).
主要成果:
- 证明了SFRBE网络在合成和现实世界燃烧的低光图像上的有效性.
- 提出的方法成功地解决了火焰,噪音和细节保存的挑战.
结论:
- 新的FLLIE任务和SFRBE网络在处理复杂的低光条件方面取得了重大进展.
- SFRBE提供了一个强大的解决方案,用于增强低照明和光闪光的图像.
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