Real-Time Grayscale Dehazing Scheme For Car Vision

Abstract

To improve the safety of autonomous cars, their obstacle detection capability in bad weather must be substantially improved. Haze is a major factor that degrades outdoor images. Although various dehazing schemes have been proposed, a dehazing scheme designed to improve obstacle detection capability has not been reported. Hence, we present a dehazing algorithm that enhances the safety of an autonomous car. This algorithm should be able to work in real time, even using edge computers typically installed as car electronics. Furthermore, this algorithm should work on grayscale images, as systems dependent on color images are often unaffected by environmental color changes caused by factors such as a setting sun. We developed this algorithm based on the following three existing dehazing algorithms: dark channel prior, median dark channel prior, and the parameter tuning scheme for dark channel prior. We extend these methods based only on grayscale images. In terms of object detection capability, structural similarity index measure, and peak signal-to-noise ratio, the empirical results showed that our grayscale image-based proposed algorithm is comparable to the results of current cutting-edge methods, and operates in real time.