"Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In the recent past, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) and reduce the health hazard in X-Ray Computed CT. The book with be suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra"--Provided by publisher  
Abstract: Greedy algorithms -- Sparse recovery -- Co-sparse recovery -- Group sparsity -- Joint sparsity -- Low-rank matrix recovery -- Combined sparse and low-rank recovery -- Dictionary learning -- Medical imaging -- Biomedical signal reconstruction -- Regression -- Classification -- Computational imaging -- Denoising

"Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In the recent past, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) and reduce the health hazard in X-Ray Computed CT. The book with be suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra"--Provided by publisher