SWT and PCA image fusion methods for multi-modal imagery

Abstract

Image fusion is the process of combining two or more related images to produce a single output image, containing more relevant information than any one of the input images. The image-fusion process depends upon: the application domain; the number of images undergoing fusion; and the type of imagery, such as whether it is multi-spectral or multi-modal. For clarity of presentation, this paper takes two important fusion methods, Stationary Wavelet Transform (SWT) and Principal Components Analysis (PCA), and applies them to a variety of imagery. Results show that in multi-modal image fusion, PCA appears to perform better for those input images that have different contrast/brightness levels. SWT appears to give better performance when the input images are multi-modal and multi-sensor. A feature of the paper are the number of objective functions employed to evaluate the SWT and PCA methods, allowing the utility of each to be judged. The reader will also find in this paper a concise guide to image fusion techniques with clear recommendations on how to evaluate them.