Advances in Sampling Theory and Techniques

Advances in Sampling Theory and Techniques provides both the most updated formulations of the sampling theory and practical algorithms of image sampling with sampling rates close to the theoretical minimum, as well as interpolation-error-free methods of image resampling and the theory of discrete representation of signal integral transforms. Topics include classical sampling theory, compressed sensing, non-redundant sampling, fast signal resampling algorithms, the discrete uncertainty principle, digital convolution, and various versions of discrete Fourier transforms.

Exercises based in MATLAB supplement the text throughout, and the sample code is provided to go alongside discussions. The book also includes examples using Image Processing Toolbox.