Introduction - If you have any usage issues, please Google them yourself
This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms,
and non-linear multiscale transforms based on the median and mathematical
morphology operators. Recent concepts of sparsity and morphological diversity are described
and exploited for various problems such as denoising, inverse problem regularization,
sparse signal decomposition, blind source separation, and compressed sensing.
This book weds theory and practice in examining applications in areas such as astronomy,
biology, physics, digital media, and forensics. A final chapter explores a paradigm
shift in signal processing, showing that previous limits to information sampling and
extraction can be overcome in very significant ways.