Department of Image Processing

Linear least squares minimization with sparsity-inducing regularization is a common task in image restoration problems (denoising, deconvolution,...). However, the nonlinear least squares with L1-regularization are something everybody is afraid of. In this tutorial, an efficient solution of nonlinear restoration problems will be presented. The solution is based on proximal minimization theory and the algorithm will be applied to the problem of estimation of perfusion parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.