{"corpus_id":10349990,"paper_sha":"844236f08ea07371c71b7da2d8183fbd4ac5d799","doi":"10.1109/CVPR.2008.4587647","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":2161516371,"dblp_id":"conf/cvpr/YangWHM08","acl_id":null,"title":"Image super-resolution as sparse representation of raw image patches","year":2008,"publication_date":"2008-06-23","venue":"2008 IEEE Conference on Computer Vision and Pattern Recognition","journal":{"name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","pages":"1-8","volume":null},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle","Conference"],"pubmed_pub_types":null,"s2_fields_of_study":["Mathematics","Computer Science","Engineering"],"reference_count":29,"citation_count":1666,"influential_citation_count":145,"is_open_access":true,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":"http://www.ee.ucsc.edu/%7Emilanfar/IS08/MaPaper.pdf","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/844236f08ea07371c71b7da2d8183fbd4ac5d799","s2_open_access_license":null,"s2_open_access_status":"GREEN","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"This paper addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image. We approach this problem from the perspective of compressed sensing. The low-resolution image is viewed as downsampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the downsampled signal. We will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high-resolution image is competitive or even superior in quality to images produced by other SR methods.","claims":[{"public_id":"cl_43f91fbabfd4684cff67645fb4bbc6a6","status":"active","text":"A small set of randomly chosen raw patches from training images of similar statistical nature can serve as a good dictionary for super-resolution.","confidence":0.95,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_43f91fbabfd4684cff67645fb4bbc6a6"},{"public_id":"cl_221cd03419fb233e3305c617b6fd7529","status":"active","text":"Compressed sensing provides the framework under which the sparse representation of 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