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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Haji, Alim"],"institution":null,"degree_name":"Optical Science and Engineering","degree_level":"Masters","degree_discipline":"Optical Science and Engineering","degree_department":null,"school":null,"contributors":["Osinski, Marek","Hayat, Majeed","Matson, Charles"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-09-09T07:00:00Z","date_published":"2007-09-09T07:00:00Z","updated_at":"2026-07-24T05:27:04Z","subjects":["Image reconstruction--Digital techniques","Image processing--Digital techniques.","Other Engineering"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalrepository.unm.edu/ose_etds/52","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Osinski, Marek","Hayat, Majeed","Matson, Charles"]},{"key":"dc:creator","label":"Author","values":["Haji, Alim"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Optical Science and Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters","Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Optical Science and Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Image reconstruction--Digital techniques","Image processing--Digital techniques.","Other Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalrepository.unm.edu/ose_etds/52"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Cramer-Rao bound analysis of multi-frame blind deconvolution"]}]}],"canonical_facts":{"dc:contributor":["Osinski, Marek","Hayat, Majeed","Matson, Charles"],"dc:creator":["Haji, Alim"],"dc:description.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.
"],"dc:identifier":["https://digitalrepository.unm.edu/ose_etds/52"],"dc:language":["English"],"dc:subject":["Image reconstruction--Digital techniques","Image processing--Digital techniques.","Other Engineering"],"dc:title":["Cramer-Rao bound analysis of multi-frame blind deconvolution"],"thesis:degree_discipline":["Optical Science and Engineering"],"thesis:degree_level":["Masters","Thesis"],"thesis:degree_name":["Optical Science and Engineering"]},"updated_at":"2026-07-24T05:27:04Z"}