Single-Image Specular Highlight Removal via Real-World Dataset Construction

Abstract

Specular reflections pose great challenges on various multimedia and computer vision tasks, e.g., image segmentation, detection and matching. In this paper, we build a large-scale Paired Specular-Diffuse (PSD) image dataset, where the images are carefully captured by using real-world objects and the ground-truth specular-free diffuse images are provided. To the best of our knowledge, this is the first real-world benchmark dataset for specular highlight removal task, which is useful for evaluating and encouraging new deep learning-based approaches. Given this dataset, we present a novel Generative Adversarial Network (GAN) for specular highlight removal from a single image by introducing the detection of specular reflection information as a guidance. Our network also makes full use of the attention mechanism and is able to directly model the mapping relation between the diffuse area and the specular highlight area without any explicit estimation of the illumination. Experimental results demonstrate that the proposed network is more effective to remove specular reflection components with the guidance of specular highlight detection than recent state-of-the-art methods.