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.
Download count: 0
- Partial requirement for: Ph.D., Arizona State University, 2018Note typethesis
- Includes bibliographical references (pages 163-174)Note typebibliography
- Field of study: Mathematics