Anisotropic Nonlocal Means Denoising

Abstract

It has recently been proved that the popular nonlocal means (NLM) denoising algorithm does not optimally denoise images with sharp edges. Its weakness lies in the isotropic nature of the neighborhoods it uses to set its smoothing weights. In response, in this paper we introduce several theoretical and practical anisotropic nonlocal means (ANLM) algorithms and prove that they are near minimax optimal for edge-dominated images from the Horizon class. On real-world test images, an ANLM algorithm that adapts to the underlying image gradients outperforms NLM by a signi cant margin.

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Document Details

Document Type
Technical Report
Publication Date
Nov 26, 2011
Accession Number
ADA556952

Entities

People

  • Arian Maleki
  • Manjari Narayan
  • Richard G. Baraniuk

Organizations

  • Rice University

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Contrast
  • Directional
  • Estimators
  • Filters
  • Image Processing
  • Inequalities
  • Noise
  • Observation
  • Orientation (Direction)
  • Probability
  • Random Variables
  • Risk
  • Risk Analysis
  • Simulations
  • Standards

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Fluid Dynamics (CFD)
  • Computer Vision.