Why Doesn't Compressive Sensing Work for Any Signal?

1. How does compressed sensing work?
2. Where is compressed sensing used?
3. What is compressive sensing theory?
4. What is compressed sensing in MRI?

How does compressed sensing work?

Compressed sensing addresses the issue of high scan time by enabling faster acquisition by measuring fewer Fourier coefficients. This produces a high-quality image with relatively lower scan time. Another application (also discussed ahead) is for CT reconstruction with fewer X-ray projections.

Where is compressed sensing used?

In a wide range of applications, the compressive sensing can be applied, like data mining [6,7], text processing [8], signal processing [9,10], agriculture on-board data processing [11,12], image enhancement [13,14], acoustic OFDM [15], medical image processing [16–18], image adaptation [19] Electrocardiogram ...

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 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.