Illumination-Invariant Color Image Correction

Abstract

This paper presents a new statistical approach for learning automatic color image correction. The goal is to parameterize color independently of illumination and to correct color for changes of illumination. This is useful in many image processing applications, such as color image segmentation or background subtraction. The motivation for using a learning approach is to deal with changes of lighting typical of indoor environments such as home and office. The method is based on learning color invariants using a modified multi-layer perceptron (MLP). The MLP is odd-layered and the central bottleneck layer includes two neurons that estimates the color invariants and one input neuron proportional to the luminance desired in output of the MLP(luminance being strongly correlated with illumination). The advantage of the modified MLP over a classical MLP is better performance and the estimation of invariants to illumination. Results compare the approach with other color correction approaches from the literature.