Ali Ahmed

This page provides software to generate the figures and the experiments in the paper 'Blind Deconvolution using Convex Programming', shown in the reference below. 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.

 

Matlab scripts

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

 

References

The above figures were generated for the paper:

  • A. Ahmed, B. Recht, and J. Romberg, "Blind Deconvolution using Convex Programming", submitted to IEEE Transactions on Information Theory, 2012.

The Noiselet toolbox is by Professor Romberg, while the minFunc toolbox is taken from the following paper:

  • M. Schmidt, "minFunc: unconstrained differentiable multivariate optimization in Matlab". minFunc, 2012.

If you use either of these files in your personal work, please remember to cite these references.