AUTOMATIC IMAGE EQUALIZATION AND CONTRAST ENHANCEMENT USING GAUSSIAN MIXTURE MODELING
In this paper an adaptive image equalization algorithm is introduced it automatically enhances the contrast in an input image. It uses the Gaussian mixture model to model the image gray level distribution In this model the intersection points of the Gaussian components are used to partition the dynamic range of the image into input gray level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray level distribution is also used to weight the components in the mapping of the input interval to the output interval. Simulation results prove that the proposed algorithm gives better enhanced images than several stateof the art algorithms.