{"id":{"repo_id":"byu","oai_identifier":"oai:scholarsarchive.byu.edu:etd-1210"},"canonical_url":"https://search.dev.ndltd.org/etd/byu/oai:scholarsarchive.byu.edu:etd-1210","repository":{"repo_id":"byu","name":"Brigham Young University","base_url":"https://scholarsarchive.byu.edu/do/oai/"},"display":{"title":"Real-time Image Enhancement Using Texture Synthesis","abstract":"<p>This thesis presents an approach to real-time image enhancement using texture synthesis. Traditional image enhancement techniques are typically time consuming, lack realistic detail, or do not scale well for large magnification factors.</p><p>Real-time Enhancement using Texture Synthesis (RETS) combines interpolation, classification, and patch-based texture synthesis to enhance low-resolution imagery, particularly aerial imagery. RETS uses as input a low-resolution source image and several high-resolution sample textures. The output of RETS is a high-resolution image with the structure of the source image, but with detail consistent with the high-resolution sample textures. We show that RETS can enhance large amounts of imagery in real-time. Our implementation can produce over twenty-five million pixels per second on an average PC.</p>","abstract_html":"<p>This thesis presents an approach to real-time image enhancement using texture synthesis. Traditional image enhancement techniques are typically time consuming, lack realistic detail, or do not scale well for large magnification factors.</p><p>Real-time Enhancement using Texture Synthesis (RETS) combines interpolation, classification, and patch-based texture synthesis to enhance low-resolution imagery, particularly aerial imagery. RETS uses as input a low-resolution source image and several high-resolution sample textures. The output of RETS is a high-resolution image with the structure of the source image, but with detail consistent with the high-resolution sample textures. We show that RETS can enhance large amounts of imagery in real-time. Our implementation can produce over twenty-five million pixels per second on an average PC.</p>","abstract_has_math":false,"creators":["Sorensen, Matthew J."],"institution":"Brigham Young University - Provo","degree_name":"MS","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T01:27:34Z","subjects":["computer","graphics","texture","synthesis","terrain","real-time","Computer Sciences"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsarchive.byu.edu/etd/211","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Sorensen, Matthew J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2004-11-16T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["Brigham Young University - Provo"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer","graphics","texture","synthesis","terrain","real-time","Computer Sciences"]}]},{"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://scholarsarchive.byu.edu/etd/211","https://scholarsarchive.byu.edu/context/etd/article/1210/viewcontent/ETD_CISOPTR_230.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Physical and Mathematical Sciences; Computer Science"]},{"key":"dc:description.abstract","label":"Abstract","values":["<p>This thesis presents an approach to real-time image enhancement using texture synthesis. Traditional image enhancement techniques are typically time consuming, lack realistic detail, or do not scale well for large magnification factors.</p><p>Real-time Enhancement using Texture Synthesis (RETS) combines interpolation, classification, and patch-based texture synthesis to enhance low-resolution imagery, particularly aerial imagery. RETS uses as input a low-resolution source image and several high-resolution sample textures. The output of RETS is a high-resolution image with the structure of the source image, but with detail consistent with the high-resolution sample textures. We show that RETS can enhance large amounts of imagery in real-time. 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RETS uses as input a low-resolution source image and several high-resolution sample textures. The output of RETS is a high-resolution image with the structure of the source image, but with detail consistent with the high-resolution sample textures. We show that RETS can enhance large amounts of imagery in real-time. Our implementation can produce over twenty-five million pixels per second on an average PC.</p>"],"dc:format":["application:pdf"],"dc:identifier":["https://scholarsarchive.byu.edu/etd/211","https://scholarsarchive.byu.edu/context/etd/article/1210/viewcontent/ETD_CISOPTR_230.pdf"],"dc:language":["English"],"dc:publisher":["Brigham Young University - Provo"],"dc:source":["Brigham Young University - Provo"],"dc:subject":["computer","graphics","texture","synthesis","terrain","real-time","Computer Sciences"],"dc:title":["Real-time Image Enhancement Using Texture Synthesis"],"dc:type":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T01:27:34Z"}