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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.

Advanced Driver Assistance System (ADAS) in combination with other active safety features like air bags etc. is gaining popularity. Vision based ADAS systems perform well under ideal lighting, illumination and environmental conditions. However, with change in illumination and other lighting related factors, the effectiveness of vision based ADAS systems tend to deteriorate. Under conditions of low light, it is therefore important to develop techniques that would offset the effects of low illumination and generate an image that appears as if it were taken under ideal lighting conditions. To accomplish this, we have developed a method, that uses local color statistics from the host image with low illumination, and enhance the same using an adaptive color transfer mechanism. By taking cues from the properties of ideal images that are saved in a database, the proposed method tends to recreate the input scene (with low illumination), into a near ideal scene, based on the database images.