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Abstract #5088

Total Variation Denoising with Spatially Dependent Regularization

Florian Knoll1, Yiqiu Dong2, Christian Langkammer3, Michael Hintermller2,4, Rudolf Stollberger1

1Institute of Medical Engineering, Graz University of Technology, Graz, Austria; 2Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria; 3Department of Neurology, Medical University Graz, Graz, Austria; 4Department of Mathematics, Humboldt-University of Berlin, Berlin, Germany

The Total Variation regularization model is popular in MR research. In this model, a regularization parameter controls the trade-off between noise elimination, and preservation of image details. However, MR images are comprised of multiple details. This indicates that different amounts of regularization are desirable for regions with fine image details in order to obtain better restoration results. This work introduces spatially dependent regularization parameter selection for TV based image restoration. With this technique, the regularization parameter is adapted automatically based on the details in the images, which improves the reconstruction of details as well as providing an adequate smoothing for the homogeneous parts.