I've posted some sample code on Github for performing image deblurring in Matlab using Mex. I wrote it as a way to play around with the ADMM algorithm for sparse signal reconstruction, as described in Stephen Boyd's ADMM paper, as well as to get some experience using C++ code from Matlab.
The code uses matrix-free linear operators to evaluate the forward and reverse measurement (blurring) process, solving the data-term subproblem with gradient descent. While you can do this much faster using FFTs for deconvolution, the code should generalize quite well to tomography with non-orthographic projections. I've tried to comment it well so that I can figure out what I did if I ever need to pick it up again.
In the image above, the left plot is the ground-truth image and the right is blurred by a point-spread function and corrupted by Gaussian noise. After running the demo, the following results are obtained:
The demo is pretty quick to run and is helpful for choosing parameters since you can see how the behavior of the method changes with the regularizer weight and the penalty term weight.
You can download the code from https://github.com/jamesgregson/MexADMMDeblurDemo
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