Blind Deconvolution using Modulated Inputs

This page provides software to generate the figures and the experiments in the paper Blind Deconvolution using Modulated Inputs. We also provide software created by other groups which is necessary to run our own code.

Required Toolboxes

The following toolboxes are required to run the MATLAB scripts below. The paths to the associated directories need to be provided in the script.

• Noiselet Toolbox

Matlab scripts

We provide the Matlab scripts that generate the figures, as well as a test file that demonstrates large scale blind deconvolution using modulated inputs.

• Script to generate Figure 4 (phase transition M vs K): Figure4a.m

  • Requires Noiselet Toolbox
  • Requires: adjcA1d.m
  • Requires: concat1d.m

• Script to generate Figure 4 (phase transition N vs K): Figure4f.m

  • Requires Noiselet Toolbox
  • Requires: adjcA1d.m
  • Requires: concat1d.m

• Script to generate Figures5 (image deblurring): Figure5.m

  • Requires: Noiselet Toolbox
  • Requires: adjcA2d.m
  • Image data: concat2d.m

• Script to generate Figure 6 (recovery in the presence of noise): Figure6_left.m

  • Requires: Noiselet Toolbox
  • Requires: adjcA1d.m
  • Requires: concat1d.m

• Script to generate Figure 6 (oversampling): Figure6_right.m

  • Requires: Noiselet Toolbox

  • Requires: adjcA1d.m

  • Requires: concat1d.m

References

The Noiselet toolbox is by Professor Justin Romberg, if you use either of these files in your personal work, please remember to cite this reference.