School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China.
Sensors (Basel). 2022 Sep 2;22(17):6656. doi: 10.3390/s22176656.
Highlight removal is a fundamental and challenging task that has been an active field for decades. Although several methods have recently been improved for facial images, they are typically designed for a single image. This paper presents a lightweight optimization method for removing the specular highlight reflections of multi-view facial images. This is achieved by taking full advantage of the Lambertian consistency, which states that the diffuse component does not vary with the change in the viewing angle, while the specular component changes the behavior. We provide non-negative constraints on light and shading in all directions, rather than normal directions contained in the face, to obtain physically reliable properties. The removal of highlights is further facilitated through the estimation of illumination chromaticity, which is done by employing orthogonal subspace projection. An important practical feature of the proposed method does not require face reflectance priors. A dataset with ground truth for highlight removal of multi-view facial images is captured to quantitatively evaluate the performance of our method. We demonstrate the robustness and accuracy of our method through comparisons to existing methods for removing specular highlights and improvement in applications such as reconstruction.
去除高光一直是一个基本而具有挑战性的任务，几十年来一直是活跃的研究领域。尽管最近已经有几种方法被改进用于面部图像，但它们通常是针对单张图像设计的。本文提出了一种轻量级的优化方法，用于去除多视角面部图像的镜面高光反射。这是通过充分利用朗伯一致性来实现的，即漫反射分量不随视角的变化而变化，而镜面分量则改变行为。我们在所有方向上对光和阴影提供非负约束，而不是包含在面部中的法向方向，以获得物理上可靠的属性。通过采用正交子空间投影来估计光照色度，进一步促进了高光的去除。所提出的方法的一个重要实际特点是不需要面部反射率先验知识。我们使用带有多视角面部图像高光去除的地面实况的数据集来定量评估我们方法的性能。我们通过与现有的去除镜面高光的方法进行比较，并在重建等应用中进行改进，展示了我们方法的鲁棒性和准确性。
