An application of a proximal method with a fractional Laplacian regularizer for inverse problems

A fractional Laplacian based regularization method for solving inverse problem. This new regularization method will be compared on reconstruction speed and quality compared to already existing reconstruction algorithms. This comparison is done using the Radon transform and autoconvolution as operators. Using the Radon transform the fractional Laplacian will be compared to TV-regularization and with the autoconvolution it will be compared to FISTA.