Is this project an undergraduate, graduate, or faculty project?
Undergraduate
group
What campus are you from?
Daytona Beach
Authors' Class Standing
Thomas Pasfield, Junior Madeline Gorman, Junior Eleanor Sigel, Junior
Lead Presenter's Name
Thomas Pasfield
Faculty Mentor Name
Mihhail Berezovski
Abstract
Image blur often hinders radiographic data analysis. By approaching image deblurring as an ill-posed inverse problem, methods such as Tikhonov and Total Variation Regularization provide ample solutions. As non-blind methods, the regularization requires a Point Spread Function for computation and thus must be estimated. In this work, we present a comprehensive analysis of the uncertainty propagation of these processes. The findings improve the utility of regularization in image deblurring across various fields.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, Spark Grant
Uncertainty Propagation in Image Deblurring
Image blur often hinders radiographic data analysis. By approaching image deblurring as an ill-posed inverse problem, methods such as Tikhonov and Total Variation Regularization provide ample solutions. As non-blind methods, the regularization requires a Point Spread Function for computation and thus must be estimated. In this work, we present a comprehensive analysis of the uncertainty propagation of these processes. The findings improve the utility of regularization in image deblurring across various fields.