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arXiv · 2304.08350

Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction

Andreas Kofler·Fabian Altekrüger·Fatima Antarou Ba·Christoph Kolbitsch·Evangelos Papoutsellis·David Schote·Clemens Sirotenko·Felix Frederik Zimmermann·Kostas Papafitsoros
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Abstract

We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps.

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Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba, Christoph Kolbitsch, Evangelos Papoutsellis, David Schote, Clemens Sirotenko, Felix Frederik Zimmermann, Kostas Papafitsoros. 2023-04-17. Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction. https://arxiv.org/abs/2304.08350

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