Efficient road specular reflection removal based on gradient properties

Abstract

Highlights caused by changes in sunlight throughout any given day cause failure in stereo matching, object recognition, and road segmentation. This is a serious challenge in advanced driver assistance systems (ADAS), because local high brightness and color discontinuities generally result in noticeable blurring of the road surface or object. This paper presents a novel strategy for removing specular reflection from highlight images by gradients distribution to optimize the diffuse image. The dark channel is introduced as a prior to initially estimate and locate the highlight. The threshold filter is then adopted to divide the high-intensity highlight and the weak highlight - the weak highlight affect neither the stereo matching nor road segmentation process. Finally, gradient properties (varying smoothness of specular and diffuse reflections) are presented to optimize the layer separation. Experimental results in speed and accuracy of road segmentation show that proposed method outperforms other techniques for separating highlights from road surfaces.