ABSTRACT
This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book explains the mathematical methods used for image reconstruction. It examines the concept of a system by developing several different models of systems. The major models are convolution, simultaneous linear equations, and stochastic processes. The book then deals with the various transformation methods. It also deals with image reconstruction. The book focuses on the linear systems theory and linear algebra. It then introduces some basic statistical methods. The book then describes the discrete Fourier transform and details how digital computers perform the Fourier transform operation. It describes vector space rotation and explains the two-dimensional Fourier transform, the Laplace transform, and the z transform. The book also highlights filtering stochastic signals, which is important for understanding image reconstruction in the presence of noise and develops the analogy between linear algebra methods and stochastic filtering.
