{"id":"https://openalex.org/W4221153133","doi":"https://doi.org/10.48550/arxiv.2203.14297","title":"Learning Graph Regularisation for Guided Super-Resolution","display_name":"Learning Graph Regularisation for Guided Super-Resolution","publication_year":2022,"publication_date":"2022-03-27","ids":{"openalex":"https://openalex.org/W4221153133","doi":"https://doi.org/10.48550/arxiv.2203.14297"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2203.14297","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14297","pdf_url":"https://arxiv.org/pdf/2203.14297","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.14297","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064838766","display_name":"Riccardo de Lutio","orcid":"https://orcid.org/0000-0002-2644-3876"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"de Lutio, Riccardo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101699278","display_name":"Alexander Becker","orcid":"https://orcid.org/0000-0002-1943-2984"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Becker, Alexander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008566906","display_name":"Stefano D\u2019Aronco","orcid":"https://orcid.org/0000-0003-0142-1731"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D'Aronco, Stefano","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080029458","display_name":"Stefania Russo","orcid":"https://orcid.org/0000-0003-0114-8042"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Russo, Stefania","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071121905","display_name":"Jan Dirk Wegner","orcid":"https://orcid.org/0000-0002-0290-6901"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wegner, Jan D.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005404030","display_name":"Konrad Schindler","orcid":"https://orcid.org/0000-0002-3172-9246"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schindler, Konrad","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5064838766"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7410542964935303},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.66999751329422},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6462444067001343},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.548635721206665},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.4824236333370209},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4595645070075989},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4366290867328644},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.42680495977401733},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39282190799713135},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37664395570755005},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13209092617034912}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7410542964935303},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.66999751329422},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6462444067001343},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.548635721206665},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.4824236333370209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4595645070075989},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4366290867328644},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42680495977401733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39282190799713135},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37664395570755005},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13209092617034912},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2203.14297","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14297","pdf_url":"https://arxiv.org/pdf/2203.14297","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2203.14297","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2203.14297","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2203.14297","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14297","pdf_url":"https://arxiv.org/pdf/2203.14297","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4221153133.pdf","grobid_xml":"https://content.openalex.org/works/W4221153133.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W4285277090","https://openalex.org/W2103419012","https://openalex.org/W4327738859","https://openalex.org/W2988126442","https://openalex.org/W1974414866","https://openalex.org/W2381850946","https://openalex.org/W2981583398"],"abstract_inverted_index":{"We":[0,175],"introduce":[1],"a":[2,11,18,65,136],"novel":[3],"formulation":[4],"for":[5,83],"guided":[6,84],"super-resolution.":[7],"Its":[8],"core":[9],"is":[10,99,117,123],"differentiable":[12],"optimisation":[13,42,166],"layer":[14,167],"that":[15,203],"operates":[16],"on":[17,180],"learned":[19,23,134],"affinity":[20],"graph.":[21],"The":[22],"graph":[24,41,121,156],"potentials":[25,132,172],"make":[26],"it":[27,158],"possible":[28,160],"to":[29,53,60,73,125,161,209],"leverage":[30],"rich":[31,143],"contextual":[32],"information":[33,145],"from":[34,79,173],"the":[35,39,44,50,54,58,62,74,96,120,126,129,154,165,170],"guide":[36],"image,":[37],"while":[38,194],"explicit":[40],"within":[43],"architecture":[45],"guarantees":[46],"rigorous":[47],"fidelity":[48],"of":[49,114,153,190],"high-resolution":[51],"target":[52],"low-resolution":[55],"source.":[56,97],"With":[57],"decision":[59],"employ":[61],"source":[63],"as":[64,70],"constraint":[66],"rather":[67],"than":[68],"only":[69,93,101],"an":[71],"input":[72],"prediction,":[75],"our":[76,115,178,204],"method":[77,116,179,205],"differs":[78],"state-of-the-art":[80],"deep":[81,137],"architectures":[82],"super-resolution,":[85],"which":[86],"produce":[87],"targets":[88],"that,":[89,118],"when":[90],"downsampled,":[91],"will":[92],"approximately":[94],"reproduce":[95],"This":[98],"not":[100,212],"theoretically":[102],"appealing,":[103],"but":[104],"also":[105,195],"produces":[106],"crisper,":[107],"more":[108],"natural-looking":[109],"images.":[110],"A":[111],"key":[112],"property":[113],"although":[119],"connectivity":[122],"restricted":[124],"pixel":[127],"lattice,":[128],"associated":[130],"edge":[131,171],"are":[133],"with":[135],"feature":[138],"extractor":[139],"and":[140,168,183],"can":[141],"encode":[142],"context":[144],"over":[146],"large":[147],"receptive":[148],"fields.":[149],"By":[150],"taking":[151],"advantage":[152],"sparse":[155],"connectivity,":[157],"becomes":[159],"propagate":[162],"gradients":[163],"through":[164],"learn":[169],"data.":[174],"extensively":[176],"evaluate":[177],"several":[181],"datasets,":[182],"consistently":[184],"outperform":[185],"recent":[186],"baselines":[187],"in":[188],"terms":[189],"quantitative":[191],"reconstruction":[192],"errors,":[193],"delivering":[196],"visually":[197],"sharper":[198],"outputs.":[199],"Moreover,":[200],"we":[201],"demonstrate":[202],"generalises":[206],"particularly":[207],"well":[208],"new":[210],"datasets":[211],"seen":[213],"during":[214],"training.":[215]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
