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Research article accepted for the Proceedings of the European Signal Processing Conference (EUSIPCO) 2022


Andreas Kofler (PTB) published together with Christian Wald (Charité–Universitätsmedizin Berlin), Markus Haltmeier (University of Innsbruck), Tobias Schäffter (PTB) and Christoph Kolbitsch (PTB) an article about a method for image reconstruction based on convolutional dictionary learning.

The method can be seen as a physics-informed and supervised method fo learnind a convolutional dictionary. By unrolling an iterative scheme which is derived from a functional which involves a convolutional dictionary as regularization method, the filters as well all regularization parameters can then be learned in a supervised manner by end-to-end training of the network. The method was evaluated on a cardiac cine MR reconstruction problem and compared to other machine learning-based methods.

A pre-print of the article is available at https://arxiv.org/abs/2206.04447. An implementation of the method as well as its analysis counterpart (see https://arxiv.org/abs/2203.02166) can be found at https://github.com/koflera/ConvSparsityNNs.


Christoph Kolbitsch, E-Mail: Opens local program for sending emailChristoph.Kolbitsch(at)ptb.de