A review on various image fusion algorithms

The process of Image fusion can be defined as the process of combining multiple input images into a single composite image. Our aim is to create a single output image from the collection of input images which contains a better explanation of the view than the one provided by any of the individual input images. The fundamental problem of image fusion is one of determining the best procedure for combining the several input images. The review adopted in this paper is that combining various images with prior information is best handled within a statistical outline. The manuscript presented here is a representative collection of the latest advances in the field of Image Fusion (IF) which is offered and a range of methods that are causative to its growth are presented. It describes the spatial and transforms domain fusion techniques such as, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Discrete Cosine Transform (DCT) and wavelet domain techniques and others.