Modeling and Estimation of Spatially-Varying Point-Spread Functions due to Lens Aberrations and Defocus

Master's Thesis

Abstract

Many image restoration and analysis approaches in the literature rely on an accurate characterization of the linear blur kernel for an image, the point-spread function (PSF). Existing PSF models are either parameterized and spatially-invariant, or spatially-varying and discretely-defined.

In this thesis, we propose a parameterized, spatially-varying PSF model to describe the blur due to lens aberrations and defocus. The model follows from the combination of several geometric camera models, and the Seidel third-order aberration model.

We propose a novel estimation algorithm for computing the parameters of the aberration model from a set of PSF observations, and we demonstrate through simulation that this yields a more reliable set of PSF estimates. In simulated PSF sets with spread measure noise as strong as 10 dB SNR, the proposed model consistently led to PSF estimates with a 5 dB SNR improvement over the observations, and typically a 10 dB SNR improvement.

Attributes

Attribute NameValues
URN
  • etd-12082011-145645

Author Jonathan Simpkins
Advisor Dr. Robert L. Stevenson
Contributor Dr. Robert L. Stevenson, Committee Chair
Contributor Dr. Ken Sauer, Committee Member
Contributor Dr. J. Nicholas Laneman, Committee Member
Degree Level Master's Thesis
Degree Discipline Electrical Engineering
Degree Name MSEE
Defense Date
  • 2011-12-01

Submission Date 2011-12-08
Country
  • United States of America

Subject
  • point-spread functions

  • Seidel aberrations

  • image deconvolution

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units

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