Total Deep Variation for Linear Inverse Problems

Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock

Research output: Contribution to conferencePaperpeer-review

Abstract

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.
Original languageEnglish
Pages7546-7555
Number of pages10
DOIs
Publication statusPublished - 5 Aug 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2020 - virtuell, Virtual, United States
Duration: 14 Jun 202019 Jun 2020

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2020
CountryUnited States
CityVirtual
Period14/06/2019/06/20

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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