Advanced Driver Assistance Systems (ADAS) have become an inevitable part of most of the modern cars. Their use is mandated by regulations in some cases; and in other cases where vehicle owners have become more safety conscious. Vision / camera based ADAS systems are widely in use today. However, it is to be noted that the performance of these systems is depends on the quality of the image/video captured by the camera. Low illumination is one of the most important factors which degrades image quality. In order to improve the system performance under low illumination, it is required to first enhance the input images/frames. In this paper, we propose an image enhancement algorithm that would automatically enhance images to a near ideal condition. This is accomplished by mapping features taken from images acquired under ideal illumination conditions on to the target low illumination images/frames.The proposed method consists of four steps a) Pre- processing b) a coarse level segmentation of the input image, c) searching for an appropriate images from the database and d) adaptive color transfer. Since our algorithm performs appropriate adaptive modification to various regions, the quality of resultant image is good even under low illumination condition. We have also done a quantitative evaluation using the entropy and contrast based measures. Results prove that our method performs much better both quantitatively and qualitatively when compared to the standard state of the art image enhancement tools that are widely used.