基于适应性亮度调整的低光色彩图像均等化
1Artificial Intelligence College, Zhejiang Industry & Trade Vocational College, Wenzhou, 325003, China. jxlihaohong@163.com.
Scientific reports
|December 8, 2025
概括
这项研究使用多色化和自适应性马校正来增强低光色彩图像. 该方法在自动驾驶和安全监控等应用中显著提高了亮度和细节.
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:
- 低亮度彩色图像遭受昏暗和细节差,限制安全和自动驾驶应用.
- 现有的方法与不均的照明和亮度波动作斗争.
研究的目的:
- 为了提高低光下彩色图像的质量.
- 为了解决昏暗的亮度和不均的局部照明的问题.
- 为了改善实际应用的图像质量.
主要方法:
- 采用多分组消光和自适应性马校正来实现亮度统一.
- 使用盲源分离无效化和皮尔森增长曲线调整.
- 采用双级自适应性马校正,用于单不均照明.
主要成果:
- 平均亮度从5.03-25.31增加到57.14-80.02.,从5.03-25.31增加到57.14-80.02.
- 信息度从3.26提高到6.07-8.19. 这是一个很大的变化.
- 全尺寸图像的处理时间为85.06毫秒.
结论:
- 该方法有效地解决了低亮度图像中的亮度波动和局部过度曝光.
- 在图像亮度,细节和视觉保真方面取得了显著的改进.
- 在关键应用中为低光图像增强提供了可行的解决方案.
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