Artificial Haze Immune Algorithm for Image Processing

Yanhui Guo, Lin Meng, Xiaobing Tang, Yufeng Shi, Han Cao, Y. Bai
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Abstract

The Intelligent Transportation Systems bring a safely and comfortable motorized society, which are based on image processing such as predicting/detecting the danger of vehicles collecting the transport information to control the traffic flow on traffic control systems etc. However, with the pollution of environment, the fog/haze becomes a serious problem, causing the image deterioration or degradation and the Intelligent Transportation Systems lose their functions. This paper proposed an immunological method of image processing for detecting the fog/haze in the image to support Intelligent Transportation Systems. This state-of-the-art method is based on the theory of biological defence system, which unites the reducing of fog/haze and edge detection. The experimental results show that our proposed haze immunized algorithm can remove effect of haze on image processing algorithm. Compared to conventional de-haze image processing algorithm, our proposed algorithm unitizes bio-inspired algorithm to achieve efficient hardware consumption. The results of FPGA implementation show less hardware usage than conventional method.
图像处理中的人工雾免疫算法
智能交通系统通过对交通控制系统进行图像处理,预测/检测车辆的危险,收集交通信息,控制交通流量,从而带来了一个安全、舒适的机动化社会。然而,随着环境的污染,雾霾成为一个严重的问题,导致图像恶化或退化,智能交通系统失去了功能。本文提出了一种免疫图像处理方法,用于检测图像中的雾/霾,以支持智能交通系统。这种最先进的方法是基于生物防御系统的理论,它结合了减少雾/霾和边缘检测。实验结果表明,本文提出的雾霾免疫算法能够消除雾霾对图像处理算法的影响。与传统的去雾图像处理算法相比,我们提出的算法结合了仿生算法,实现了高效的硬件消耗。FPGA实现的结果表明,与传统方法相比,硬件占用更少。
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