What is the difference between compressed sensing and compressive sensing?
These are two terms that are often used interchangeably to refer to the same field of research. Which is the theory and practice of acquiring and reconstructing signals using fewer measurements than traditionally required.
However, there is a subtle difference between the two terms. Compressed sensing is a broader term that refers to the general idea of acquiring and processing signals with fewer measurements than the Nyquist-Shannon sampling theorem suggests. It encompasses various methods, including sub-Nyquist sampling, sub-Nyquist coding, and compressive sensing.
On the other hand, compressive sensing is a specific technique for acquiring and processing signals that are sparse or compressible in some domain. It relies on the idea that many signals of interest have a sparse representation in some transform domain. In addition can become accurately recovered from a small number of linear measurements. Compressive sensing is typically implemented using a sensing matrix. One that maps the high-dimensional signal onto a lower-dimensional space, and a reconstruction algorithm that recovers the original signal from the compressed measurements.
Moreover, in practice, the terms often become used interchangeably, and the distinction between the two is not always clear-cut. In conclusion, both terms refer to the same general concept of acquiring and processing signals with fewer measurements than traditionally required. And lastly, both rely on the principle of sparsity or compressibility of the signal.
What is the difference between compressed sensing and compressive sensing?
