Shepp-Logan phantom

by Yubo "Paul" Yang

Compressive Sensing

Compressive sensing takes advantage of sparsity to reconstruct full signal from sparse samples in a way that is not limited by Nyquist-Shannon. It effectively performs compression at the time of sensing so that few detector/sensors are needed. It has many practical applications such as single-pixel camera, digital-to-analog conversion, and lattice dynamics in atomic simulations.

Presentation Summary

In these slides, I present:

  • Basic idea behind the compressive sensing (cs).
  • A few simple examples.


  • simplest CS implementation.
  • beat Nyquist-Shannon frequency for perfect reconstruction.
  • reconstruct Shepp-Logan phantom. You also need to generate the phantom image and config_h5 to store the constructed A matrix (or code is pretty slow).


All Signal Processing

signal processing