In this paper, we propose a general framework to estimate the spectrum of the illumination from global specular information in a single hyperspectral image. By utilizing the specular independent subspace, we iteratively separate the reflectance components and shape a weight scheme in order to find specular-contaminated pixels. After that, the illumination can be directly estimated by factorizing the weighted specular-contaminated pixels. The proposed method enables a direct and effective decomposition of the illumination and reflectance components from a single hyperspectral image. We demonstrate the robustness and accuracy of our method on simulation and real experiments. Moreover, we capture a hyperspectral image dataset with ground-truth illumination to quantitative compare the performance.

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.