  In this paper, a method for enhancing low contrast images is proposed. This
method, called Gaussian Mixture Model based Contrast Enhancement (GMMCE),
brings into play the Gaussian mixture modeling of histograms to model the
content of the images. Based on the fact that each homogeneous area in natural
images has a Gaussian-shaped histogram, it decomposes the narrow histogram of
low contrast images into a set of scaled and shifted Gaussians. The individual
histograms are then stretched by increasing their variance parameters, and are
diffused on the entire histogram by scattering their mean parameters, to build
a broad version of the histogram. The number of Gaussians as well as their
parameters are optimized to set up a GMM with lowest approximation error and
highest similarity to the original histogram. Compared to the existing
histogram-based methods, the experimental results show that the quality of
GMMCE enhanced pictures are mostly consistent and outperform other benchmark
methods. Additionally, the computational complexity analysis show that GMMCE is
a low complexity method.
