Deep Convolutional Neural Network for Inverse Problems in Imaging

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyperparameter selection. The starting point of this paper is the observation that unrolled iterative methods have the form of a CNN (filtering followed by pointwise nonlinearity) when the normal operator (H*H, where H* is the adjoint of the forward imaging…

• MC
  Mayo Clinic
• N
  Nvidia
• AA
  American Association of Physicists in Medicine
• EC
  European Commission

  Awards: H2020-ERC, H2020, 692726, 665667
• ÉP
  École Polytechnique Fédérale de Lausanne

  Award: 665667
• PS
  Paul Scherrer Institut
• CD
  Centre d'Imagerie BioMédicale
• H2
  Horizon 2020 Framework Programme

  Award: 665667
• NI
  National Institute of Biomedical Imaging and Bioengineering

  Awards: EB017095, EB017185
• HE
  H2020 European Research Council