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 infor-
mation 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.