Compressed sensing in image processing

1. What is compressed sensing in image processing?
2. What is meant by compressed sensing?
3. What is the use of compressive sensing?
4. What is compressed sensing in machine learning?

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.

What is meant by compressed sensing?

Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems.

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 compressed sensing in machine learning?

Compressed sensing (CS) is a technique in signal processing which reconstructs any given signal at a rate less than that of Nyquist's' rate given that the signal is sparse and incoherent in nature. The main focus of CS is to find a random matrix which reconstructs the original signal using as few samples as possible.