An illumination estimation scheme for color constancy based on chromaticity histogram and neural network

This paper proposes an algorithm for estimation the illumination of an image for the purpose of color constancy using chromaticity histogram. This method evaluates the color temperature of the light source by detecting the distribution of chromaticity histogram in an image. It has the advantages of high efficiency, good robustness, and no strict assumptions. We subsequently propose to use a multilayer perceptrons trained by backpropagation algorithm to model the nonlinear functional relationship between the chromaticity histogram and coefficients of illuminant functions. The trained neural network can then be used to estimate the spectral power distribution of light source. Finally, we use the trained neural network to estimate spectral power distribution in a finite-dimensional linear model of surface reflectance for color constancy. For performance evaluation, two color-recovery experiments on synthetic images and nature images captured from a still digital camera are performed in this paper. All the results are compared to those of two existing popular algorithms (Max-RGB and Gray-World algorithms).