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.

Abstract

dvanced Driver Assistance Systems (ADAS) based on video camera tends to be generalized in today's automotive. However, if most of these systems perform nicely in good weather conditions, they perform very poorly under adverse weather particularly under rain. We present a novel approach that aims at detecting raindrops on a car windshield using only images from an in-vehicle camera. Based on the photometric properties of raindrops, the algorithm relies on image processing technics to highlight raindrops. Its results can be further used for image restoration and vision enhancement and hence it is a valuable tool for ADAS.