Entropy Enhancement Using Image Fusion Techniques Based on Laplacian and Gaussian Pyramid Decomposition

The quality of an image directly related with its visual clarity (perception quality), resolution and contrast, in image fusion process it is combined appropriate information from two or more source images into one single image such that the fused or output modified image contains most of the information from parent images. Here the two categories of image fusion algorithms are implemented, the basic function algorithm and pyramid based algorithm. Pyramid based algorithm means that Laplacian and Gaussian pyramid decomposition. The successful fusion images acquired from different instruments is great important in many applications such as medical imaging, microscopic imaging, remote sensing, computer vision and robotics. In this paper, we apply this fact effectively design a contrast enhancement method for images that improves the quality of visible image without introducing unrealistic visual appearances. Entropy Enhancement is a useful technique for improving image of brightness differences in the dark, gray or bright regions as compare to the brightness differences in the other regions. The proposed method improves the optimization problem that maximizes average local contrast and global contrast of an image.