Compressed (sparse) sensing

Question 1

Define Nyquist-Shannon sampling theorem

Answer 1

“If we sample a signal at twice its highest frequency, then we can recover it exactly.” However, if the signal is sparse fewer samples are needed for reconstruction

Question 2

Define Compressed sensing (CS) (or sparse sampling)

Answer 2

a novel paradigm in data acquisition that allows representing sparse data in an efficient and accurate way, using sparse recovery (SR) techniques based on nonlinear interpolation

Question 3

What is the key idea of compressed sensing?

Answer 3

to recover a sparse signal from very few nonadaptive,
linear measurements by convex optimization

Question 4

Examples use of compressed sensing

Answer 4

Computed tomography image reconstruction

Question 5

Steps in compressed sensing

Answer 5

1. Measure projections
2. First image estimate
3. simulated projections / corrected projections
4. comparison
5. correct images
6. iterative cycle - 3, 4, 5
7. end point - final images

Question 6

Define signal acquisition in compressed sensing