Color Correction for Digital Images Based on the Finite-Dimensional Linear-Model

The image color recorded in a computer vision system depends on three factors: the physical content of the scene, the illumination on the scene, and the characteristics of the camera. The goal of computational color correction is to reset the intensities of the color channels in digital images, and express the scene colors canonically. In the color correction method based on spectral reflectance reproduction, the finite-dimensional linear-model is an effective way to condense spectral reflectance. Since there are only three channels for each pixel in digital images, color correction based on spectral reflectance reproduction using the finite-dimensional model with three dimensions is needed. In the definite gamut, the spectral reflectance curves are analyzed using the finite-dimensional model. It is shown that reflectance generated by three basis functions which is capturing 99.13% of the overall variance, provides good approximations to the measured spectral reflectance. Together with the measured spectral power distribution of the illumination and the estimated spectral responses of the camera, the quantum catches are calculated. Errors caused by approximation of the finite-dimensional model are relatively small, which shows that color correction based on the finite-dimensional linear-model has feasibility to some extent.