Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modelling

In this paper, we propose an adaptive image equalization algorithm which automatically enhances the contrast in an input image. The algorithm uses Gaussian mixture model (GMM) to model the image grey-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input grey-level intervals. The contrast equalized image is generated by transforming the pixels’ grey levels in each input interval to the appropriate output grey-level interval according to the dominant Gaussian component and cumulative distribution function (CDF) of the input interval. To take account of human perception the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the grey-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types. Index Terms Contrast enhancement, histogram equalization, normal distribution, Gaussian mixture modelling, histogram partition.