Estimation of spectral distribution of scene illumination from a single image with chromatic illuminant

The current paper proposes an illuminant estimation algorithm that estimates the spectral power distribution of an incident light source using its chromaticity determined based on the perceived illumination and highlight method. The proposed algorithm is composed of three steps. First, the illuminant chromaticity of the global incident light is estimated using a hybrid method that combines the perceived illumination and highlight region. Second, the surface spectral reflectance is then recovered from the image after decoupling the global incident illuminant for each channel. The surface spectral reflectance calculation is limited to the maximum achromatic region (MAR), which is the most achromatic and brightest region in the image, and estimated using the principal component analysis (PCA) method along with a set of given Munsell samples. Third, the closest colors are selected from a spectral database composed of reflected-lights generated by the given Munsell samples and a set of illuminants. Finally, the illuminant of the image is calculated using the average spectral distributions of the reflected-lights selected for the MAR region and its average surface reflectance. Simulations were performed using artificial color-biased images and the results confirmed the accuracy of the estimates produced by the proposed method for various illuminants.