Gaussian mixture model-based contrast enhancement

Mohsen Abdoli Mohammad Ghanbari Hossein Sarikhani Patrice Brault 1
1 Division Télécoms et Réseaux - L2S
L2S - Laboratoire des signaux et systèmes : 1289
Abstract : 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.
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Article dans une revue
IET Image Processing, Institution of Engineering and Technology, 2015, 9 (7), pp.569-577. 〈10.1049/iet-ipr.2014.0583〉
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01253050
Contributeur : Patrice Brault <>
Soumis le : vendredi 8 janvier 2016 - 15:38:14
Dernière modification le : jeudi 5 avril 2018 - 12:30:23

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Mohsen Abdoli, Mohammad Ghanbari, Hossein Sarikhani, Patrice Brault. Gaussian mixture model-based contrast enhancement. IET Image Processing, Institution of Engineering and Technology, 2015, 9 (7), pp.569-577. 〈10.1049/iet-ipr.2014.0583〉. 〈hal-01253050〉

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