On Iterative Regularization and Its Application

Abstract

Many existing techniques for image restoration can be expressed in terms of minimizing a particular cost function. Iterative regularization methods are a novel variation on this theme where the cost function is not fixed, but rather refined iteratively at each step. This provides an unprecedented degree of control over the tradeoff between the bias and variance of the image estimate, which can result in improved overall estimates error. This useful property, along with the provable convergence properties of the sequence of estimates produced by these iterative regularization methods lend themselves to a variety of useful applications. In this paper, we introduce a general set of iterative regularization methods, discuss some of their properties and applications, and include examples to illustrate them.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA469424

Entities

People

  • Michael R. Charest Jr.
  • Peyman Milanfar

Organizations

  • University of California, Santa Cruz

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Adaptive Filters
  • Algorithms
  • Composite Images
  • Computational Complexity
  • Data Analysis
  • Electrical Engineering
  • Engineering
  • Filters
  • Gaussian Noise
  • Image Processing
  • Image Reconstruction
  • Image Restoration
  • Images
  • Iterations
  • Monte Carlo Method
  • Noise
  • Residuals

Readers

  • Computational Modeling and Simulation
  • Operations Research
  • Systems Analysis and Design