Compressive sampling and sensing allow for efficient signal acquisition and storage by capitalizing on the fact that many real-world signals inherently have far fewer degrees of freedom than the signal size might indicate. In some cases, for example, the signals of interest can be expressed as sparse linear combinations of elements from some dictionary, and the sparsity of the representation, in turn, permits efficient algorithms for signal processing. In other cases, the conciseness of the signal model may impose a low-dimensional geometric (often manifold-like) structure to the signal class as a subset of the high-dimensional ambient signal space. Some example application areas that capitalize on concise and low-dimensional geometric models include image compression; parameter estimation and image registration; imaging and signal reconstruction; and most recently, Compressive Sampling (CS). CS is an emerging theory which permits radically new sensing devices that simultaneously acquire and compress certain signals using very efficient randomized sensing protocols. The implications of the CS theory are very far-reaching and will likely impact analog-to-digital conversion, data compression, medical imaging, sensor networks, digital communication, statistical model selection, and more.|
ECE FacultyBalzano, Laura
Fessler, Jeffrey A.
Related Labs, Centers, and Groups|
Center for Compressive Sensing