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In the research field of automotive systems, Advanced Driver Assistance Systems (ADAS) are gaining paramount importance. As the significance for such systems increase, the challenges associated with it also increases. These challenges can arise due to technology, human factors, or due to natural elements (haze, fog, rain etc.). Among these, natural challenges, especially haze, pose a major setback for technologies depending on vision sensors. It is a known fact that the presence of haze in the atmosphere degrades the driver's visibility as well as the information available with the vision based ADAS. To ensure reliability of ADAS in different climatic conditions, it is vital to get back the information of the scene degraded by haze prior to analyzing the images. In this paper, the proposed work addresses this challenge with a novel and faster image preprocessing technique that can enhances the quality of haze affected images both in terms of visibility and visual perception.

Driver safety and Advanced Driver Assistance Systems (ADAS) is gaining lot of importance these days. In some countries, there are strict regulations in place which mandate the use of certain ADAS features in automobiles. However, as the need for these safety critical systems increases, the challenges associated also increase. These challenges can arise due to technology, human factors or due to nature. In countries like India, where one can expect different weather conditions with changing geography, the associated challenges are mainly due to the natural factors like haze, fog, rain and smoke. This poses a challenging problem in terms of visibility for the drivers as well as in vision based ADAS; thereby, leading to many fatal road accidents. In this paper, a novel pre-processing technique, which addresses the interesting problem of enhancing the perceptual visibility of an image that is degraded by atmospheric haze, is proposed.