Regularization of Inverse Problems via Time Discrete Geodesics in Image Spaces

Abstract

This paper addresses the solution of inverse problems in imaging given an additional reference image. We combine a modification of the discrete geodesic path model for image metamorphosis with a variational model,actually the $L^2$-$TV$ model, for image reconstruction. We prove that the space continuous model has a minimizer which depends in a stable way from the input data. Two minimization procedures which alternate over the involved sequences of deformations and images in different ways are proposed. The updates with respect to the image sequence exploit recent algorithms from convex analysis to minimize the $L^2$-$TV$ functional. For the numerical computation we apply a finite difference approach on staggered grids together with a multilevel strategy. We present proof-of-the-concept numerical results for sparse and limited angle computerized tomography as well as for superresolution demonstrating the power of the method.

References in corpus (5)

• Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems
• Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport
• Indirect Image Registration with Large Diffeomorphic Deformations
• How Strong Are Streak Artifacts in Limited Angle Computed Tomography?
• Image reconstruction through metamorphosis

Cited by in corpus (3)

• Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
• Image reconstruction through metamorphosis
• Consistent Approximation of Interpolating Splines in Image Metamorphosis