Sparse Image and Signal Processing provides the latest in sparse, multiscale image, and signal processing. This book discusses linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and nonlinear multiscale transforms based on the median and mathematical morphology operators. The text covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, inpainting, and compressed sensing.
New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geolocated data), dictionary learning, and nonnegative matrix factorization. The authors combine theory and practice when examining applications in areas such as astronomy (including recent results from the European Space Agency's Herschel mission), biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics.
Sparse Image and Signal Processing:
- Allows the reader to approach subjects through the motivation of examples, using software available for download or through theory
- Provides information that is topical, engaging, and relevant; ideal for scientists, researchers, and students in academia and commercial settings
- Features up-to-date themes, such as compressed sensing
A set of MATLAB code files are available for download to accompany these methods and applications.