ABSTRACT
Compressive sensing (CS) (Candes and Tao 2006) has recently emerged as an efficient technique for sampling a signal with fewer coefficients than dictated by classical Shannon/ Nyquist theory. The assumption underlying this approach is that the signal to be sampled must have concise representation on a convenient basis, meaning that there exists a basis where the signal can be expressed with few large coefficients and many (close-to-)zero coefficients. In CS, sampling is performed by taking a number of linear projections of the signal onto pseudorandom sequences, whereas reconstruction exploits knowledge of a domain where the signal is “sparse.”
