Towards High-Quality Specular Highlight Removal by Leveraging
  Large-Scale Synthetic Data
Gang Fu Qing Zhang Lei Zhu Chunxia Xiao Ping Li
This paper aims to remove specular highlights from a single object-level
image. Although previous methods have made some progresses, their performance
remains somewhat limited, particularly for real images with complex specular
highlights. To this end, we propose a three-stage network to address them.
Specifically, given an input image, we first decompose it into the albedo,
shading, and specular residue components to estimate a coarse specular-free
image. Then, we further refine the coarse result to alleviate its visual
artifacts such as color distortion. Finally, we adjust the tone of the refined
result to match that of the input as closely as possible. In addition, to
facilitate network training and quantitative evaluation, we present a
large-scale synthetic dataset of object-level images, covering diverse objects
and illumination conditions. Extensive experiments illustrate that our network
is able to generalize well to unseen real object-level images, and even produce
good results for scene-level images with multiple background objects and
complex lighting.