A Multidimensional Histogram Equalization by Fitting an Isotropic Gaussian Mixture to a Uniform Distribution

In this paper, a novel method to extend the grayscale histogram equalization (GHE) for color images in a multi-dimension is proposed. Unlike most current techniques, the proposed method can generate a uniform histogram, thus minimizing the disparity between the histogram and uniform distribution. A histogram of any dimension is regarded as a mixture of isotropic Gaussians. This method is a natural extension of the GHE to a multi-dimension. An efficient algorithm for the histogram equalization is provided. The results show that this approach is valid, and a psycho-visual study on a target distribution will improve the practical use of the proposed method.