Image processing techniques primarily focus upon enhancing the quality of an image or a set of images and to derive the maximum information from them. Image Fusion is such a technique of producing a superior quality image from a set of available images. It is the process of combining relevant information from two or more images into a single image wherein the resulting image will be more informative and complete than any of the input images. A lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection, Image processing, parallel and distributed processing, Robotics and remote sensing. This paper reports a detailed study performed over a set of image fusion algorithms regarding their implementation. The paper explains the theoretical and implementation issues of the efficient image fusion algorithms considered and the experimental results of the same. The fusion algorithms were assessed based on the study and development of some image quality metrics. Reported is the study and implementation of image quality metrics that were developed for assessing the image fusion algorithms implemented. The experimental results have been discussed in detail and the inference thus arrived at. The paper, in the later section describes about the image fusion toolkit called Wavelet Fusion and Laplacian Fusion, developed using MATLAB, providing a graphical user interface for the same. A description is provided about the usage of the toolkit in order to fuse the set of input images using the various image fusion algorithms.