Howtosignalprocessing
- What is sparsity in signal processing?
- What is meant by compressed sensing?
- What is compressed sensing in image processing?
- What is compressed sensing in MRI?
What is sparsity in signal processing?
A signal is considered sparse if most of its information is contained within a few non-zero samples. Consequently, a signal reconstruction algorithm has to find a sparse vector that best represents the measured signal. Many algorithms to solve this problem are based on l1-norm optimization.
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 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 compressed sensing in MRI?
Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI.
