自适应对比度增强涉及基于cnn的雾天条件和不均匀光照条件处理

Christopher Schwarzlmuller, Fadi Al Machot, A. Fasih, K. Kyamakya
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引用次数: 5

摘要

在高级驾驶辅助系统(ADAS)的背景下,自适应图像处理是一个至关重要的问题,因为恶劣的天气条件会导致视力下降。在有雾的天气里,由于散射光产生的空气光的存在,图像对比度和能见度很低,而空气光又由雾粒子引起。由于基于视觉的ADAS受到对比度不足的影响,因此需要具有实时功能的解决方案。为了改善这种退化图像,需要一种对每个图像区域分别进行处理的方法。因此,需要实时处理,该方法采用具有实时图像处理特性的CNN范式实现。为了与现有的最先进的方法进行比较,采用了Tenengrad方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive contrast enhancement involving CNN-based processing for foggy weather conditions & non-uniform lighting conditions
Adaptive image processing in the context of Advanced Driver Assistance Systems (ADAS) is a crucial issue because bad weather conditions lead to poor vision. In a foggy weather, image contrast and visibility are low due to the presence of airlight that is generated by scattering light, which in turn is caused by fog particles. Since vision based ADAS are affected by inadequate contrast, a real-time capable solution is required. To improve such degraded images, a method is required which processes each image region separately. Hence, real-time processing is required, the method is realized with the CNN paradigm which claims the characteristic of real-time image processing. To compare the proposed method with existing state-of-the-art methods the Tenengrad measure is applied.
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