Gaussian Mixture Model Based Contrast Enhancement

TL;DR

This paper introduces GMMCE, a novel contrast enhancement technique that models image histograms with Gaussian mixtures, effectively broadening low contrast images while maintaining quality and low computational complexity.

Contribution

The paper presents a new GMM-based method for contrast enhancement that outperforms existing histogram-based techniques in quality and efficiency.

Findings

Enhanced images show improved contrast and quality.

GMMCE outperforms benchmark methods in experiments.

The method has low computational complexity.

Abstract

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.