Gaussian mixture model-based contrast enhancement

Mohsen Abdoli (1) , Hossein Sarikhani (1) , Mohammad Ghanbari (2) , Patrice Brault (3)

1 Sharif University of Technology - Department of Computer Engineering
2 University of Essex
3 L2S - Laboratoire des signaux et systèmes

In this study, 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 modelling of histograms to model the content of the images. On the basis of 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 optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with 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 shows that GMMCE is a low-complexity method.

Mots clés: Unsupervised image contrast enhancement; Multi-Gaussian histogram model.; Model-based contrast enhancement

Domaines: Traitement du signal et de l'image [eess.SP]

Mohsen Abdoli, Hossein Sarikhani, Mohammad Ghanbari, Patrice Brault. Gaussian mixture model-based contrast enhancement. IET Image Processing, 2015, 9 (7), pp.569-577. ⟨10.1049/iet-ipr.2014.0583⟩. ⟨hal-01253050⟩