The new edition of this classic book gives all the major concepts, techniques and applications of
sparse representation, reflecting the key role the subject plays in today's signal processing.
The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms,
and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained
and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse
representations in redundant dictionaries, super-resolution and compressive sensing applications.
A Wavelet Tour of Signal Processing: The Sparse Way, third edition,
is an invaluable resource for researchers and R/D engineers wishing to apply the theory
in fields such as image processing, video processing and compression, bio-sensing,
medical imaging, machine vision and communications engineering.
Stephane Mallat is Professor in Applied Mathematics at Ecole Polytechnique, Paris, France.
From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University,
and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company.