The authors discuss the spectral estimation of multiple light sources from image data in a complex illumination environment. An approach is proposed to effectively estimate illuminant spectra and the corresponding light sources based on highlight areas that appear on dielectric object surfaces. First, the authors develop a highlight detection method using two types of convolution filters with Gaussian distributions, center-surround and low-pass filters. This method is available even for white surfaces, and it is independent of object color and of viewing and incidence angles. Second, they present an algorithm for estimating the illuminant spectra from extracted highlight areas. Each specular highlight area has a spectral composition corresponding to only one light source among multiple light sources. The spectral image data are projected onto a two-dimensional subspace, where a linear cluster in pixel distribution is detected for each highlight area. Third, the relative positional relationship between highlight areas among different object surfaces is used to identify the light sources on each surface. The authors develop an algorithm based on probabilistic relaxation labeling. The light source for each highlight and the corresponding spectral-power distribution are determined from the iterative labeling process. Finally, the feasibility of the proposed approach is examined in an experiment using a real complex environment, where dielectric objects are illuminated by multiple light sources of light-emitting diode, fluorescence, and incandescence.
White balancing is a fundamental step in the image processing pipeline. The process involves estimating the chromaticity of the illuminant source and using the estimate to correct the image to remove any color cast. Given the importance of the problem, there has been much previous work on illuminant estimation. Previous work is either more accurate but slow and complex, or fast and simple but less accurate. In this paper, we propose a method for illuminant estimation that uses (i) fast features known to be predictive in illuminant estimation and (ii) single feature decision boundaries in ensembles of multivariate regression trees, (iii) each of which has been constructed to minimize a multivariate distance measure appropriate for illuminant estimation. The result is an illuminant estimation method that is simultaneously fast, simpler, and more accurate.