{"id":{"repo_id":"strathclyde","oai_identifier":"oai:strathclyde:vt150j34f"},"canonical_url":"https://search.dev.ndltd.org/etd/strathclyde/oai:strathclyde:vt150j34f","repository":{"repo_id":"strathclyde","name":"University of Strathclyde","base_url":"https://stax.strath.ac.uk/catalog/oai"},"display":{"title":"Super-resolution image reconstruction from low-resolution images","abstract":"The thesis addresses the problem of obtaining high-resolution image from a set of one or more low-resolution images. The thesis focused on three building blocks of super-resolution algorithms i.e., image registration for super-resolution, image fusion for super-resolution and super-resolution image reconstruction. These three parts are addressed separately and singular value decomposition-based fusion is introduced before performing interpolation or single-image super-resolution. An accurate image registration is crucial for super-resolution. An image registration approach for super-resolution based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is described to automatically register the low-resolution images. The results have shown effective for the removal of the mismatched features in the image. A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. The proposed method has shown to reduces the computational time and enhance the prior estimation of the high-resolution image as well as the final super-resolution results. The performance of the algorithms is evaluated using synthetic sequences and also on real sequences subjectively and objectively.","abstract_html":"The thesis addresses the problem of obtaining high-resolution image from a set of one or more low-resolution images. The thesis focused on three building blocks of super-resolution algorithms i.e., image registration for super-resolution, image fusion for super-resolution and super-resolution image reconstruction. These three parts are addressed separately and singular value decomposition-based fusion is introduced before performing interpolation or single-image super-resolution. An accurate image registration is crucial for super-resolution. An image registration approach for super-resolution based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is described to automatically register the low-resolution images. The results have shown effective for the removal of the mismatched features in the image. A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. The proposed method has shown to reduces the computational time and enhance the prior estimation of the high-resolution image as well as the final super-resolution results. The performance of the algorithms is evaluated using synthetic sequences and also on real sequences subjectively and objectively.","abstract_has_math":false,"creators":["Nasir, Haidawati Mohamad"],"institution":"University of Strathclyde","degree_name":"phd","degree_level":"doctoral-pg","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-24T04:53:25Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.48730/57k6-dt88"],"render_values":[{"text":"10.48730/57k6-dt88","href":"https://doi.org/10.48730/57k6-dt88","code":true}]},{"key":"dc:identifier","label":"Identifier","values":["T13127"],"render_values":[{"text":"T13127","href":null,"code":true}]}]},"links":{"outbound_url":"https://stax.strath.ac.uk/concern/theses/vt150j34f","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Nasir, Haidawati Mohamad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012"]},{"key":"dc:date.issued","label":"Date","values":["2012"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electronic and Electrical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Strathclyde"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral-pg"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["T13127"]},{"key":"dc:identifier.doi","label":"DOI","values":["10.48730/57k6-dt88"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://stax.strath.ac.uk/concern/theses/vt150j34f"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Strathclyde theses - ask staff. 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A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. The proposed method has shown to reduces the computational time and enhance the prior estimation of the high-resolution image as well as the final super-resolution results. The performance of the algorithms is evaluated using synthetic sequences and also on real sequences subjectively and objectively."]},{"key":"dc:description.abstract","label":"Abstract","values":["The thesis addresses the problem of obtaining high-resolution image from a set of one or more low-resolution images. The thesis focused on three building blocks of super-resolution algorithms i.e., image registration for super-resolution, image fusion for super-resolution and super-resolution image reconstruction. These three parts are addressed separately and singular value decomposition-based fusion is introduced before performing interpolation or single-image super-resolution. An accurate image registration is crucial for super-resolution. An image registration approach for super-resolution based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is described to automatically register the low-resolution images. The results have shown effective for the removal of the mismatched features in the image. A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. The proposed method has shown to reduces the computational time and enhance the prior estimation of the high-resolution image as well as the final super-resolution results. 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An image registration approach for super-resolution based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is described to automatically register the low-resolution images. The results have shown effective for the removal of the mismatched features in the image. A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. 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