Deep Specular Highlight Removal for Single Real-world Image

Zhongqi Wu, Chuanqing Zhuang, Jian Shi, Jun Xiao, Jianwei Guo

Abstract: Specular highlight removal is a challenging task. We present a novel data-driven approach for automatic specular highlight removal from a single image. To this end, we build a new dataset of real-world images for specular highlight removal with corresponding ground-truth diffuse images. Based on the dataset, we also present a specular highlight removal network by introducing the detection of specular reflections information as guidance. The experimental evaluations indicate that the proposed approach outperforms recent state-of-the-art methods.