What is the best way to separate data using compressive sensing?

1. What is the use of compressive sensing?
2. What is compressive sensing theory?
3. What is compressed sensing in image processing?
4. Who invented compressive sensing?

What is the use of compressive sensing?

Compressive sensing (CS) offers compression of data below the Nyquist rate, making it an attractive solution in the field of medical imaging, and has been extensively used for ultrasound (US) compression and sparse recovery. In practice, CS offers a reduction in data sensing, transmission, and storage.

What is compressive sensing theory?

The compressive sensing theory states that the signal can be reconstructed using just a small set of randomly acquired samples if it has a sparse (concise) representation in certain transform domain.

What is compressed sensing in image processing?

Compressed sensing (CS) is an image acquisition method, where only few random measurements are taken instead of taking all the necessary samples as suggested by Nyquist sampling theorem. It is one of the most active research areas in the past decade.

Who invented compressive sensing?

Background. Although the term compressed sensing (compressive sensing) was coined only recently with the paper by Donoho [26], followed by a huge research activity, such a development did not start out of thin air.