Chapter # IMAGE FUSION USING THE EXPECTATION-MAXIMIZATION ALGORITHM AND A GAUSSIAN MIXTURE MODEL

1. INTRODUCTION Image fusion refers the process of combining multiple images of a scene to obtain a single composite image [1-4]. The different images to be fused can come from different sensors of the same basic type or they may come from different types of sensors. The composite image should contain a more useful description of the scene than provided by any of the individual source images. This fused image should be more useful for human visual or machine perception. In recent years, image fusion has become an important and useful technique for image analysis, computer vision [4-7], concealed weapon detection (CWD) [8,9], and autonomous landing guidance (ALG) [10-11]. A simple image fusion method is to take the average of the source images pixel by pixel. While simple this approach can produce several undesired side effects including reduced contrast. In recent years, many researchers recognized that multiscale transforms are very useful for analyzing the information content of images for the purpose of fusion [12-14]. Several multiscale transforms have become very popular. These include t he Laplacian pyramid transform [15], the contrast pyramid transform [16-17], the gradient pyramid transform [18], and the wavelet transform [12,14,19]. At the same time, some sophisticated image fusion approaches based on