The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity. In this chapter we presented a software solution to image stabilization based on fusing the visual information between multiple frames of the same scene. The main components of the algorithm, global image registration and image fusion have been presented in detail along with several visual examples. An efficient coarse to fine image based registration solution is obtained by preserving an over-sampled version of each pyramid level in order to simplify the warping operation in each iteration step. Next the image fusion step matches the visual similar image blocks between the available frames coping thereby with the presence of moving objects in the scene or with the inability of the global registration model to describe the camera motion. The advantages of such a software solution against the popular hardware opto-mechanical image stabilization systems include: (i) the ability to prevent blur caused by moving objects in a dynamic scene, (ii) the ability to deal with longer exposure times and stabilized not only high frequency vibrations but also low frequency camera motion during the integration time, and (iii) the reduced cost and size required for implementation in small mobile devices. The main disadvantage is the need to capture multiple images of the scene. However, nowadays most camera devices provide a "burst" mode that ensures fast capturing of multiple images. Future work would have to address several other applications that can take advantage of the camera "burst" mode by fusing multiple images captured with similar of different exposure and focus parameters
