Image contrast enhancement using Gaussian Mixture model and genetic algorithm

This paper deals with the contrast improvement of an image so as to increase the clarity of image. The paper will adapt an image equalization technique through which an input image will be automatically enhanced with respective to contrast. The techniques we will be using are Gaussian Mixture model and Genetic Algorithm which are based on the Histogram Equalization process and measuring the intensity of spatial edges. The Histogram Equalization will focus on the enhancement of the image by changing the gray-level distribution. The Genetic Algorithm will search a best solution in the spatial domain so that it provides the image enhancement with good and natural contrast. The intersection points in the Gaussian Mixture Algorithm for the Gaussian Components are very important for the partition of dynamic range of image. The contrast is equalized and enhanced with the helping of mapping functions between input image and output image. The mapping is done for the intervals of input image by taking the gray-level distribution in to the picture. The Gaussian Mixture Model Algorithm is advantageous as it provides a better enhancement than the other state of the art algorithms. The genetic algorithm is advantageous because it does not produce unnatural brightness.