Cramer-Rao bound analysis of multi-frame blind deconvolution

<p>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.</p>

Name thesis:degree_name

Optical Science and Engineering

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Optical Science and Engineering

Year

2007

Author dc:creator

• Haji, Alim

Contributors dc:contributor

• Osinski, Marek
• Hayat, Majeed
• Matson, Charles

• Image reconstruction--Digital techniques
• Image processing--Digital techniques.
• Other Engineering

Language dc:language

English

Repository record dc:identifier

https://digitalrepository.unm.edu/ose_etds/52

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:ose_etds-1051