Turbo compressed sensing with partial DFT sensing matrix

This repository contains the implementation of the Turbo compressed sensing (Turbo-CS) algorithm proposed in the paper:

J. Ma, X. Yuan and L. Ping, "Turbo Compressed Sensing with Partial DFT Sensing Matrix," in IEEE Signal Processing Letters, vol. 22, no. 2, pp. 158-161, Feb. 2015, doi: 10.1109/LSP.2014.2351822.https://doi.org/10.1109/ACCESS.2017.2697978)

Introduction

Turbo-CS is a turbo compressed sensing algorithm with partial discrete Fourier transform (DFT) sensing matrices.

Code Structure

TurboCS.m: Denosing based Turbo compressed sensing algorithm.

• Input parameters
  • lambda: sparsity rate
  • N: input sparse vector size
  • M: measurement number
  • NSIM: simulation times
  • SNRdB: noise level
  • Iteration: iteration times

Table_sparsity04.mat: lookup table for the state evolution of TurboCS algorithm, generated only for sparsity rate of 0.4

Citation

@ARTICLE{6883198,
  author={J. {Ma} and X. {Yuan} and L. {Ping}},
  journal={IEEE Signal Processing Letters}, 
  title={Turbo Compressed Sensing with Partial DFT Sensing Matrix}, 
  year={2015},
  volume={22},
  number={2},
  pages={158-161},
  doi={10.1109/LSP.2014.2351822}}

Test Result

Run TurboCS.m, you will get the recovery result of Turbo-CS and its state evolution as shown below: