Description

Inverse problems model real world phenomena from data, where the data are often noisy and models contain errors. This leads to instabilities, multiple solution vectors and thus ill-posedness. To solve

Inverse problems model real world phenomena from data, where the data are often noisy and models contain errors. This leads to instabilities, multiple solution vectors and thus ill-posedness. To solve ill-posed inverse problems, regularization is typically used as a penalty function to induce stability and allow for the incorporation of a priori information about the desired solution. In this thesis, high order regularization techniques are developed for image and function reconstruction from noisy or misleading data.

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    Date Created
    • 2018
    Resource Type
  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2018
      Note type
      thesis
    • Includes bibliographical references (pages 163-174)
      Note type
      bibliography
    • Field of study: Mathematics

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    by Theresa Scarnati

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