Total Deep Variation for Linear Inverse Problems

Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock

Publikation: KonferenzbeitragPaperBegutachtung


Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term. In this paper, we propose a novel learnable general-purpose regularizer exploiting recent architectural design patterns from deep learning. We cast the learning problem as a discrete sampled optimal control problem, for which we derive the adjoint state equations and an optimality condition. By exploiting the variational structure of our approach, we perform a sensitivity analysis with respect to the learned parameters obtained from different training datasets. Moreover, we carry out a nonlinear eigenfunction analysis, which reveals interesting properties of the learned regularizer. We show state-of-the-art performance for classical image restoration and medical image reconstruction problems.
PublikationsstatusVeröffentlicht - 5 Aug. 2020
Veranstaltung2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2020 - virtuell, Virtual, USA / Vereinigte Staaten
Dauer: 14 Juni 202019 Juni 2020


Konferenz2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
KurztitelCVPR 2020
Land/GebietUSA / Vereinigte Staaten

ASJC Scopus subject areas

  • Software
  • Maschinelles Sehen und Mustererkennung

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