Estimation of Spectral Distribution of Scene Illumination from a Single Image

Abstract

This article proposes an illuminant estimation algorithm that estimates the spectral power distribution of an incident light source from a single image. The proposed illumination recovery procedure has two phases. First, the surface spectral reflectances are recovered. In this case, the surface spectral reflectances recovered are limited to the maximum achromatic region (MAR) which is the most achromatic and highly bright region of an image, after applying intermediate color constancy process using a modified gray-world algorithm. Next, the surface reflectances of the maximum achromatic region are estimated using the principal component analysis method along with a set of given Munsell samples. Second, the spectral distribution of reflected lights of MAR is selected from the spectral database. That is, a color difference is compared between the reflected lights of the MAR and the spectral database, which is the set of reflected lights built by the given Munsell samples and a set of illuminants. Then the closest colors from the spectral database are selected. Finally, the illuminant of an image can be calculated dividing the average spectral distributions of reflected lights of MAR by the average surface reflectances of the MAR. In order to evaluate the proposed algorithm, experiments with artificial and real captured color-biased scenes were performed and numerical comparison examined. The proposed method was effective in estimating the spectral distribution of the given illuminants under various illuminants and scenes without white points.