Regularized Kernel Regression for Image Deblurring

Abstract

The framework of kernel regression, a nonparametric estimation method, has been widely used in different guises for solving a variety of image processing problems including denoising and interpolation. In this paper, we extend the use of kernel regression for deblurring applications. Furthermore, we show that many of the popular image reconstruction techniques are special cases of the proposed framework. Simulation results confirm the effectiveness of our proposed methods.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA460966

Entities

People

  • Hiroyuki Takeda
  • Peyman Milanfar
  • Sina Farsiu

Organizations

  • University of California, Santa Cruz

Tags

DTIC Thesaurus Topics

  • Adaptive Optics
  • Algorithms
  • Change Detection
  • Data Sets
  • Electrical Engineering
  • Engineering
  • Errors
  • Estimators
  • Image Restoration
  • Kernel Functions
  • Noise
  • Optimization
  • Simulations
  • Standards
  • Steepest Descent Method
  • Steering
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.
  • Statistical inference.