It is also useful for practicing engineers who want to learn the concept of image fusion and use it in real life applications. No prior knowledge of image fusion is assumed, although some familiarity with basic image processing and basic tools of linear algebra is recommended. This book can also be used as an additional text for advanced image processing course. Apart from the two initial chapters, the book is divided into three parts. Part II relates to various techniques and algorithms commonly used in image fusion. Among the topics considered are: the sub-space transformation, multi-resolution analysis, wavelets, ensemble learning, bagging, enhance, color space, image thresholding, Markov random fields, image size similarity and expectation-maximization algorithm. Inside, a few examples of real-life image fusion examined in detail, including panchromatic sharpening, color image segmentation and Truth ensembles simultaneously and Performance Algorithm Warfield et al. The book is accompanied by web pages of additional material that can be obtained.