A General Iterative Regularization Framework for Image Denoising

Abstract

Many existing techniques for image denoising can be expressed in terms of minimizing a particular cost function. We address the problem of denoising images in a novel way by iteratively refining the cost function. This allows us some control over the trade-off between the bias and variance of the image estimate. The result is an improvement in the mean-squared error as well as the visual quality of the estimate. We consider four different methods of updating the cost function and compare and contrast them. The framework presented here is extendable to a very large class of image denoising and reconstruction methods. The framework is also easily extendable to deblurring and inversion as we briefly demonstrate. The effectiveness of the proposed methods is illustrated on a variety of examples.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA460941

Entities

People

  • Michael Elad
  • Michael R. Charest Jr.
  • Peyman Milanfar

Organizations

  • University of California, Santa Cruz

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • California
  • Computer Science
  • Electrical Engineering
  • Engineering
  • Gaussian Noise
  • Information Operations
  • Iterations
  • Machine Learning
  • Monte Carlo Method
  • Noise
  • Residuals
  • Simulations
  • White Noise

Readers

  • Image Processing and Computer Vision.
  • Life Cycle Cost Analysis
  • Statistical inference.