Compressed sensing or compressive sensing is a new concept in signal
processing where one measures a small number of non-adaptive linear
combinations of the signal. These measurements are usually much smaller
than the number of samples that define the signal. From these small
numbers of measurements, the signal is then reconstructed by non-linear
procedure. Compressed sensing has recently emerged as a powerful tool
for efficiently processing data in non-traditional ways. In this book,
we highlight some of the key mathematical insights underlying sparse
representation and compressed sensing and illustrate the role of these
theories in classical vision, imaging and biometrics problems.