采用基于火虫的CLAHE和多尺度聚变的多模式方法来增强水下图像
Venkata Lalitha Narla1, Gulivindala Suresh2, Chanamallu Srinivasa Rao3
1Department of Electronics and Communication Engineering, Aditya University, Surampalem, India.
Scientific reports
|November 11, 2024
概括
这项研究通过一种新的方法提高了水下图像质量. 该方法优化了对比度和清晰度,从而为探索提供了更清晰,更生动的水下视觉效果.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 水下成像技术的研究.
背景情况:
- 水下图像质量因深度,生物成分和电流等因素而降低,导致颜色扭曲,雾和低对比度.
- 现有的方法很难有效地恢复水下图像的视觉真实性.
研究的目的:
- 开发一种先进的算法,以提高水下图像的质量.
- 为了应对水下视觉数据中色彩扭曲,雾和低对比度的挑战.
主要方法:
- 一种多阶段的方法,涉及白平衡预处理,由Firefly算法优化的对比限度自适应基因图平衡 (CLAHE),以及带有指导过器校正的暗通道先决 (DCP).
- 实施一个多尺度的融合策略,以结合增强和dehazed图像,然后进行最终的CLAHE应用程序以提高可见性.
主要成果:
- 拟议的方法显著提高了水下图像质量,在标准指标上获得了高分 (例如,UIQM = 5.4875,RUIE数据集上的UCIQE = 0.6953).
- 对U45和RUIE数据集的实验结果表明,拟议的方法优于现有方法.
结论:
- 综合方法有效地恢复水下图像,同时解决对比度,颜色和清晰度问题.
- 优化的CLAHE和修改的DCP,结合多尺度融合,为水下图像增强提供了强大的解决方案.
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