Wavelet Signal Processing
Anthony Teolis · Birkhäuser Boston eBooks · 1998
A basic motivation behind transform methods is the idea that some sorts of processing are better (or perhaps only possibly) achieved in the transform domain rather than in the original signal domain, In this sense, the utility of a transform is measured by its ability to facilitate desired signal processing tasks in the transform domain via algorithms that are digitally tractable, computationally efficient, concise, and noise robust. The efficacy of general wavelet transforms comes from the fact that wavelet domain algorithms exhibit all of these benefits when dealing with signals that are characterized by their time—frequency behavior. This chapter explores applications of overcomplete wavelet transforms in problems of data compression, noise suppression, digital communication, and signal identification. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.