Abstract

One source of accidents when driving a vehicle is the presence of homogeneous and heterogeneous fog. Fog fades the colors and reduces the contrast of the observed objects with respect to their distances. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement of road images. The visibility enhancement algorithm proposed in [1] is not dedicated to road images and thus it leads to limited quality results on images of this kind. In this paper, we interpret the algorithm in [1] as the inference of the local atmospheric veil subject to two constraints. From this interpretation, we propose an extended algorithm which better handles road images by taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are its speed, the possibility to handle both color images or gray-level images, and its small number of parameters. A comparative study and quantitative evaluation with other state-of-the-art algorithms is proposed on synthetic images with several types of generated fog. This evaluation demonstrates that the new algorithm produces similar quality results with homogeneous fog and that it is able to better deal with the presence of heterogeneous fog.

Abstract

Driver assistance systems based on camera are strongly disturbed by the presence of foggy weather. The restoration of images, as pre-processing, would improve the performances of such systems. In this paper, we propose a method to restore the image contrast of foggy road scenes combining a physical approach, based on Koschmieders model and a signals approach, based on local histogram equalization. Then we optimize the parameters of our method using a simulated annealing. This method, evaluated on a reference image database, presents a significant improvement compared to other methods and gives consistent results for both homogeneous and inhomogeneous fog.