A Variational Framework for Exemplar-Based Image Inpainting

Abstract

Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is in part due to their superior performance in textured images, a known weakness of purely local methods. Local methods on the other hand have demonstrated to be very appropriate for the recovering of geometric structures such as image edges. The synthesis of both types of methods is a trend in current research. Variational analysis in particular is an appropriate tool for a unified treatment of local and non-local methods. In this work we propose a general variational framework non-local image inpainting, from which important and representative previous inpainting schemes can be derived, in addition to leading to novel ones. We explicitly study some of these, relating them to previous work and showing results on synthetic and real images.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2010
Accession Number
ADA540639

Entities

People

  • Gabriele Facciolo
  • Guillermo Sapiro
  • Pablo Arias
  • Vicent Caselles

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Computational Fluid Dynamics
  • Computational Science
  • Computations
  • Differential Equations
  • Equations
  • Estimators
  • Euler Equations
  • Geometry
  • Notation
  • Partial Differential Equations
  • Poisson Equation
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Statistical Mechanics

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Systems Analysis and Design