Image fusion is the process of fusing several input images into a single output image that better describes the scene than any of the input images could on their own. The requirement for image fusion is to achieve high resolution on panchromatic, multispectral, or actual-world images. There are numerous ways to combine images, as well as some approaches, including IHS, PCA, DWT, Laplacian pyramids, Gradient Pyramids, DCT, and SF. In a variety of applications, various digital image fusion algorithms have been created. An image that includes more information for human visual perception and is more valuable for additional vision processing is the result of image fusion, which pulls information from many images of a given scenario. Additionally, it wants to examine how image fusion methods are evaluated for quality. The concept, principles, restrictions, and benefits of each of the grey-scale picture fusion algorithms are reviewed and are then investigated at the pixel and feature levels.
Anita Chaudhary (Fri,) studied this question.