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.

翻译：本文旨在移除单物体级图像中的镜面高光。尽管现有方法已取得一定进展，其性能仍受限于现实复杂镜面高光场景。为此，我们提出一种三阶段网络架构来解决该问题。具体而言，给定输入图像后，首先将其分解为反照率、明暗与镜面残差分量以估计粗糙无高光图像；随后对粗糙结果进行精化处理以缓解颜色失真等视觉伪影；最后调整精化结果的色调，使其与输入尽可能匹配。此外，为促进网络训练与定量评估，我们构建了大规模物体级图像合成数据集，涵盖多样物体与光照条件。大量实验表明，该网络可良好泛化至未见过的真实物体级图像，甚至在包含多背景物体与复杂光照的场景级图像上也能取得优异表现。