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. The method uses HSV (Hue, saturation and brightness) color space and the Physics based haze model to retrieve scene information from haze affected image. In the proposed procedure, hue (H) value of each pixel is retained intact, while the saturation (S) value of de-hazed output images is scaled from the S value of hazy input images. In addition, the brightness (V) value of each pixel is also modified with a simple and novel method based on the depth information of the scene. The proposed method combines the intensity information as well as the spatial location of the pixel to calculate depth map. The proposed method modifies only the Saturation and Intensity channels of the HSV space, thereby reducing the computation cost for recovering the de-hazed images substantially. In comparison with other state of the art methods that are available in literature, the proposed method is shown to be faster and capable of recovering better haze-free images both in terms of visual perception and quantitative evaluation. Thus, the visibility restoration capability and reduced computation cost makes the proposed algorithm much suited for ADAS based applications.