Contrast enhancement of roads images with foggy scenes based on histogram equalization

Muna F. Al-sammaraie
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引用次数: 23

Abstract

Bad weather, particularly fog, commonly obstruct drivers from observing road conditions. This could frequently lead to a considerable number of road accidents. To avoid the problem, automatic methods have been proposed to enhance visibility in bad weather. Methods that work on visible wavelengths, based on the type of their input, can be categorized into two approaches: those using polarizing filters, and those using images taken from different fog densities. Both of the approaches require that the images are multiple and taken from exactly the same point of view. While they can produce reasonably good results, their requirement makes them impractical, particularly in real time applications, such as vehicle systems. Considering their drawbacks, our goal is to develop a method that requires solely a single image taken from ordinary digital cameras, without any additional hardware. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. The method principally uses color and intensity information. It enhances the visibility after estimating the color of skylight and the values of air light. The experimental results on real images show the effectiveness of the approach.
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基于直方图均衡化的雾天道路图像对比度增强
恶劣的天气,尤其是大雾,通常会妨碍司机观察路况。这可能经常导致相当数量的交通事故。为了避免这个问题,人们提出了在恶劣天气下提高能见度的自动方法。根据输入的类型,对可见光波长工作的方法可以分为两种:一种是使用偏振滤光片,另一种是使用从不同雾密度中拍摄的图像。这两种方法都要求图像是多重的,并且从完全相同的角度拍摄。虽然它们可以产生相当好的结果,但它们的要求使它们不切实际,特别是在实时应用程序中,例如车辆系统。考虑到它们的缺点,我们的目标是开发一种方法,只需要从普通数码相机拍摄一张图像,而不需要任何额外的硬件。几十年来,人们提出了几种图像增强技术。虽然大多数技术需要大量的进步和关键步骤,但对感知图像的结果并不令人满意。该方法主要使用颜色和强度信息。通过对天窗颜色和空气光值的估计,提高了能见度。在真实图像上的实验结果表明了该方法的有效性。
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