Variational Image Inpainting

Abstract

Inpainting is an image interpolation problem, with broad applications in image and vision analysis. This paper presents our recent efforts in developing inpainting models based on the Bayesian and variational principles. We discuss several geometric image models, their role in the construction of variational inpainting models, and the associated Euler-Lagrange PDEs and their numerical computation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA437276

Entities

People

  • Jianhong Shen
  • Tony F. Chan

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Brownian Motion
  • Calculus
  • Calculus Of Variations
  • Computational Science
  • Computations
  • Construction
  • Curvature
  • Diffusion
  • Equations
  • Fluid Dynamics
  • Geometry
  • Image Processing
  • Interpolation
  • Mathematical Analysis
  • Mathematics
  • New York
  • Variational Principles

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms