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This paper aims to remove specular highlights from a single object-levelimage. Although previous methods have made some progresses, their performanceremains somewhat limited, particularly for real images with complex specularhighlights. 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-freeimage. Then, we further refine the coarse result to alleviate its visualartifacts such as color distortion. Finally, we adjust the tone of the refinedresult to match that of the input as closely as possible. In addition, tofacilitate network training and quantitative evaluation, we present alarge-scale synthetic dataset of object-level images, covering diverse objectsand illumination conditions. Extensive experiments illustrate that our networkis able to generalize well to unseen real object-level images, and even producegood results for scene-level images with multiple background objects andcomplex lighting.

Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data