This paper proposes a novel approach to image stitching and sharpening that makes use of powerful keypoint extraction and matching techniques. For precise keypoint detection, the Scale-Invariant Feature Transform (SIFT) technique is used, while the Random Sample Consensus (RANSAC) approach generates homography, which improves alignment across input pictures. Keypoint matching creates correspondences in overlapping regions, allowing for more accurate alignment. To increase image quality, the approach utilises a novel sharpening process that draws on localised information from aligned keypoints. The experimental results show that the approach is effective in producing more precise images and accomplishing accurate image stitching. This method is a major contribution to the area, with applications in various areas such as Mars rover research, computer vision, remote sensing, and general image stitching.