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 recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) and has also helped reduce the health hazard in X-Ray Computed CT. This book is a valuable resource suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra.

  • Covers fundamental concepts of compressed sensing
  • Makes subject matter accessible for engineers of various levels
  • Focuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learning
  • Includes MATLAB examples for further development

chapter 1|8 pages


chapter 2|20 pages

Greedy Algorithms

chapter 3|27 pages

Sparse Recovery

chapter 4|12 pages

Co-sparse Recovery

chapter 5|23 pages

Group Sparsity

chapter 6|12 pages

Joint Sparsity

chapter 7|21 pages

Low-Rank Matrix Recovery

chapter 8|12 pages

Combined Sparse and Low-Rank Recovery

chapter 9|11 pages

Dictionary Learning

chapter 10|49 pages

Medical Imaging

chapter 11|9 pages

Biomedical Signal Reconstruction

chapter 12|7 pages


chapter 13|23 pages


chapter 14|8 pages

Computational Imaging

chapter 15|10 pages