{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/73058"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/73058","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning image super resolution from joint examples","abstract":"Image super-resolution (SR) aims to estimate of a high-resolution (HR) image from low-resolution (LR) input. Image priors are commonly learned to regularize the ill-posed SR problem, either using external LR-HR pairs or internal similar patterns repeating across di erent scales. We propose joint SR to adaptively combine the advantages of both external and internal SR. We de ne the two loss functions using sparse coding and epitomic matching, respectively. A corresponding adaptive weight is constructed to balance their e ect according to the reconstruction errors. Various image results demonstrate the e ectiveness of the proposed method over the existing state-of-the-art methods, which is also veri ed by our subject evaluation experiment.","abstract_html":"Image super-resolution (SR) aims to estimate of a high-resolution (HR) image from low-resolution (LR) input. Image priors are commonly learned to regularize the ill-posed SR problem, either using external LR-HR pairs or internal similar patterns repeating across di erent scales. We propose joint SR to adaptively combine the advantages of both external and internal SR. We de ne the two loss functions using sparse coding and epitomic matching, respectively. A corresponding adaptive weight is constructed to balance their e ect according to the reconstruction errors. Various image results demonstrate the e ectiveness of the proposed method over the existing state-of-the-art methods, which is also veri ed by our subject evaluation experiment.","abstract_has_math":false,"creators":["Wang, Zhangyang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Huang, Thomas S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-21T19:58:59Z","date_published":"2015-01-21T19:58:59Z","updated_at":"2026-07-22T22:26:07Z","subjects":["super-resolution","example-based learning","sparse coding","epitomic matching","subject evaluation"],"languages":["en"],"rights":["Copyright 2014 Zhangyang Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/73058","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S."]},{"key":"dc:creator","label":"Author","values":["Wang, Zhangyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-01-21T19:58:59Z","2014-12","2015-01-21"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["super-resolution","example-based learning","sparse coding","epitomic matching","subject evaluation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Zhangyang Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/73058"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Image super-resolution (SR) aims to estimate of a high-resolution (HR) image from low-resolution (LR) input. Image priors are commonly learned to regularize the ill-posed SR problem, either using external LR-HR pairs or internal similar patterns repeating across di erent scales. We propose joint SR to adaptively combine the advantages of both external and internal SR. We de ne the two loss functions using sparse coding and epitomic matching, respectively. A corresponding adaptive weight is constructed to balance their e ect according to the reconstruction errors. Various image results demonstrate the e ectiveness of the proposed method over the existing state-of-the-art methods, which is also veri ed by our subject evaluation experiment.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-11-19T14:54:13Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Wang_Zhangyang.pdf: 17046622 bytes, checksum: 2c728cdcc0b1a10f0b023234e3a8a244 (MD5)","Made available in DSpace on 2015-01-21T19:58:59Z (GMT). No. of bitstreams: 1 Zhangyang_Wang.pdf: 17046622 bytes, checksum: 2c728cdcc0b1a10f0b023234e3a8a244 (MD5)","Embargo set by: Seth Robbins for item 73247 Lift date: 2017-01-21T19:59:39Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Open"]},{"key":"dc:title","label":"Title","values":["Learning image super resolution from joint examples"]}]}],"canonical_facts":{"dc:contributor":["Huang, Thomas S."],"dc:creator":["Wang, Zhangyang"],"dc:date":["2015-01-21T19:58:59Z","2014-12","2015-01-21"],"dc:description":["Image super-resolution (SR) aims to estimate of a high-resolution (HR) image from low-resolution (LR) input. Image priors are commonly learned to regularize the ill-posed SR problem, either using external LR-HR pairs or internal similar patterns repeating across di erent scales. We propose joint SR to adaptively combine the advantages of both external and internal SR. We de ne the two loss functions using sparse coding and epitomic matching, respectively. A corresponding adaptive weight is constructed to balance their e ect according to the reconstruction errors. Various image results demonstrate the e ectiveness of the proposed method over the existing state-of-the-art methods, which is also veri ed by our subject evaluation experiment.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-11-19T14:54:13Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Wang_Zhangyang.pdf: 17046622 bytes, checksum: 2c728cdcc0b1a10f0b023234e3a8a244 (MD5)","Made available in DSpace on 2015-01-21T19:58:59Z (GMT). No. of bitstreams: 1 Zhangyang_Wang.pdf: 17046622 bytes, checksum: 2c728cdcc0b1a10f0b023234e3a8a244 (MD5)","Embargo set by: Seth Robbins for item 73247 Lift date: 2017-01-21T19:59:39Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Open"],"dc:identifier":["http://hdl.handle.net/2142/73058"],"dc:language":["en"],"dc:rights":["Copyright 2014 Zhangyang Wang"],"dc:subject":["super-resolution","example-based learning","sparse coding","epitomic matching","subject evaluation"],"dc:title":["Learning image super resolution from joint examples"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:07Z"}