Abstract

The solution of linear and sparseness-regularized imaging problems arising in optical and microwave applications is addressed in this work. To properly address the associated inverse problems, the recently introduced class of Compressive Sensing (CS) methods is discussed also through representative numerical examples aimed at pointing out the features, advantages and drawbacks of such methodologies in terms of computational efficiency, accuracy, robustness, and flexbility. A review of current trends and future developments within this research area is also provided.

© 2014 Optical Society of America