Over the last 20 years, multiscale methods and wavelets have revolutionized the field of applied mathematics by providing an efficient means of encoding isotropic phenomena. Directional multiscale systems, particularly shearlets, are now having the same dramatic impact on the encoding of multidimensional signals. Since its introduction about five years ago, the theory of shearlets has rapidly developed and gained wide recognition as the superior way of achieving a truly unified treatment in both a continuous and a digital setting. By now, it has reached maturity as a research field, with...
Over the last 20 years, multiscale methods and wavelets have revolutionized the field of applied mathematics by providing an efficient means of enc...
Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing,...
Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics...
Hilbert space frames have long served as a valuable tool for signal and image processing due to their resilience to additive noise, quantization, and erasures, as well as their ability to capture valuable signal characteristics. More recently, finite frame theory has grown into an important research topic in its own right, with a myriad of applications to pure and applied mathematics, engineering, computer science, and other areas. The number of research publications, conferences, and workshops on this topic has increased dramatically over the past few years, but no survey paper or...
Hilbert space frames have long served as a valuable tool for signal and image processing due to their resilience to additive noise, quantization, a...
Compressed sensing was introduced in 2006 as a novel technique in signal processing which exploits the sparseness or compressibility of a signal to efficiently acquire and reconstruct signals. Since then, the area attracted significant attention and the mathematical concepts of the theory are now quite well understood. Parallel to the progress in mathematics, the potential applications of compressed sensing have been explored by many international groups of engineers and applied mathematicians, achieving very promising advances in various areas such as communication theory, imaging sciences,...
Compressed sensing was introduced in 2006 as a novel technique in signal processing which exploits the sparseness or compressibility of a signal to ef...
This contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing. Topics considered revolve around timely aspects of compressed sensing with a special focus on applications, including compressed sensing-like approaches to deep learning; bilinear compressed sensing - efficiency, structure, and robustness; structured compressive sensing via neural network learning; compressed sensing for massive MIMO; and security of future communication and compressive sensing.
This contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing....
