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Optical Science and Engineering ETDs
Publication Date
9-9-2007
Abstract
This thesis explores how support constraints and multiple frames affect multi-frame blind deconvolution. Previous research in non-blind deconvolution, which seeks to estimate an object from a blurred and noisy image, characterized how the use of support constraints exploited spatial noise correlations to reduce noise in the estimate of the object. In multi-frame blind deconvolution, the blurring function is unknown and must be estimated along with the object. Applying a support constraint to both the object and the blurring functions, when using blind deconvolution, is one way to ensure a unique solution. The effects on the estimate of the object as a function of the size of the supports are analyzed. Also, the benefit in noise reduction in the estimate of the object from including multiple blurred and noisy images is considered. Cramer-Rao Bound theory is employed to provide an algorithm-independent metric to analyze the effects from these parameters. The Cramer-Rao bound is a lower limit to the variance of any estimate of an unknown parameter. In this research, the unknown parameters are the intensities of the object which is estimated.
Degree Name
Optical Science and Engineering
Level of Degree
Masters
Department Name
Optical Science and Engineering
First Committee Member (Chair)
Hayat, Majeed
Second Committee Member
Matson, Charles
Keywords
Image reconstruction--Digital techniques, Image processing--Digital techniques.
Document Type
Thesis
Language
English
Recommended Citation
Haji, Alim. "Cramer-Rao bound analysis of multi-frame blind deconvolution." (2007). https://digitalrepository.unm.edu/ose_etds/52