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A fast and effective vision enhancement method for single foggy image
Engineering Science and Technology, an International Journal ( IF 5.7 ) Pub Date : 2021-05-14 , DOI: 10.1016/j.jestch.2021.03.014
Pooja Pandey , Rashmi Gupta , Nidhi Goel

Images captured in foggy weather conditions suffers from inevitable problems such as low contrast, blurriness and less visibility. In many computer and vision applications like surveillance, object detection, object tracking and navigation, these low-quality images cannot be used and it requires improvement in the image quality. Different algorithms have been proposed in this direction and to upgrade the quality of a foggy image. Most of the existing methods gives good quality image but with high time complexity. In this paper, a novel and effective method is proposed to remove fog from a single image. The proposed method is based on principal component analysis and modified dark channel prior. In proposed algorithm, foggy image is pre-processed using principal component analysis. This pre-processed image is further enhanced using fast global smoothening filter. Time complexity of the proposed method is much less as compared to the various existing methods and at the same time, quality is also maintained. Also, proposed algorithm does not require a large data set and specific hardware. To see the effectiveness of the proposed technique, both qualitative and quantitative analysis has been done on synthetic data set as well as on natural dataset.



中文翻译:

一种快速有效的单雾图像视觉增强方法

在有雾的天气条件下拍摄的图像会遇到不可避免的问题,例如对比度低、模糊和能见度低。在许多计算机和视觉应用中,如监控、物体检测、物体跟踪和导航,这些低质量的图像无法使用,需要提高图像质量。在这个方向上已经提出了不同的算法,以提升有雾图像的质量。大多数现有方法都提供了高质量的图像,但时间复杂度很高。在本文中,提出了一种新颖有效的方法来去除单个图像中的雾气。所提出的方法基于主成分分析和修正的暗通道先验。在所提出的算法中,使用主成分分析对有雾图像进行预处理。使用快速全局平滑滤波器进一步增强了该预处理图像。与现有的各种方法相比,所提出的方法的时间复杂度要低得多,同时也保持了质量。此外,所提出的算法不需要大数据集和特定硬件。为了查看所提出技术的有效性,已经对合成数据集和自然数据集进行了定性和定量分析。

更新日期:2021-05-14
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