{"id":"https://openalex.org/W7140868387","doi":"https://doi.org/10.48550/arxiv.2603.24198","title":"RefReward-SR: LR-Conditioned Reward Modeling for Preference-Aligned Super-Resolution","display_name":"RefReward-SR: LR-Conditioned Reward Modeling for Preference-Aligned Super-Resolution","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7140868387","doi":"https://doi.org/10.48550/arxiv.2603.24198"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24198","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.24198","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130673538","display_name":"Yushuai Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Yushuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130640274","display_name":"Weize Quan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quan, Weize","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130654759","display_name":"Weining Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Weining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130638074","display_name":"Jiahui Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130677415","display_name":"Jing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130648640","display_name":"Meng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Meng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030475505","display_name":"Pengbin Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Pengbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100595771","display_name":"Zhentao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhentao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130694370","display_name":"Wei Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088111426","display_name":"Lunxi Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Lunxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130718745","display_name":"Dong-ming Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Dong-ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.5361999869346619,"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"}},"topics":[{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.5361999869346619,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2540000081062317,"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"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.024700000882148743,"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/consistency","display_name":"Consistency (knowledge bases)","score":0.745199978351593},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6258000135421753},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6230000257492065},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5414999723434448},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.45170000195503235},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.40230000019073486},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3776000142097473},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3716999888420105}],"concepts":[{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.745199978351593},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7045000195503235},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6258000135421753},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6230000257492065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6103000044822693},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5414999723434448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4876999855041504},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.40230000019073486},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30720001459121704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.28119999170303345},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C37279795","wikidata":"https://www.wikidata.org/wiki/Q2492305","display_name":"Consistency model","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24198","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.24198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24198","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5552889704704285,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,125],"generative":[3],"super-resolution":[4],"(SR)":[5],"have":[6],"greatly":[7],"improved":[8],"visual":[9,210],"realism,":[10],"yet":[11],"existing":[12],"evaluation":[13],"and":[14,23,123,151,168,209,214],"optimization":[15],"frameworks":[16],"remain":[17],"misaligned":[18],"with":[19,158,176,196],"human":[20,64,197],"perception.":[21],"Full-Reference":[22],"No-Reference":[24],"metrics":[25],"often":[26],"fail":[27],"to":[28,38,63],"reflect":[29],"perceptual":[30,207],"preference,":[31],"either":[32],"penalizing":[33],"semantically":[34],"plausible":[35],"details":[36],"due":[37],"pixel":[39],"misalignment":[40],"or":[41,87],"favoring":[42],"visually":[43],"sharp":[44],"but":[45],"inconsistent":[46],"artifacts.":[47],"Moreover,":[48],"most":[49],"SR":[50,173],"methods":[51],"rely":[52],"on":[53,84,96,148],"ground-truth":[54],"(GT)-dependent":[55],"distribution":[56],"matching,":[57],"which":[58],"does":[59],"not":[60],"necessarily":[61],"correspond":[62],"judgments.":[65],"In":[66],"this":[67,131],"work,":[68],"we":[69,133],"propose":[70],"RefReward-SR,":[71],"a":[72,105,113,126],"low-resolution":[73],"(LR)":[74],"reference-aware":[75],"reward":[76,181],"model":[77,174],"for":[78,142,183],"preference-aligned":[79,184],"SR.":[80],"Instead":[81],"of":[82,112],"relying":[83],"GT":[85],"supervision":[86],"NR":[88],"evaluation,":[89],"RefReward-SR":[90,177],"assesses":[91],"high-resolution":[92],"(HR)":[93],"reconstructions":[94,200],"conditioned":[95],"their":[97],"LR":[98,102],"inputs,":[99],"treating":[100],"the":[101,109,136,156,179],"image":[103],"as":[104,178],"semantic":[106,121,203],"anchor.":[107],"Leveraging":[108],"visual-linguistic":[110],"priors":[111],"Multimodal":[114],"Large":[115],"Language":[116],"Models":[117],"(MLLM),":[118],"it":[119],"evaluates":[120],"consistency":[122,150,204],"plausibility":[124,208],"reasoning-aware":[127],"manner.":[128],"To":[129],"support":[130],"paradigm,":[132],"construct":[134],"RefSR-18K,":[135],"first":[137],"large-scale":[138],"LR-conditioned":[139,165],"preference":[140],"dataset":[141],"SR,":[143],"providing":[144],"pairwise":[145],"rankings":[146],"based":[147],"LR-HR":[149],"HR":[152],"naturalness.":[153,211],"We":[154],"fine-tune":[155],"MLLM":[157],"Group":[159],"Relative":[160],"Policy":[161],"Optimization":[162],"(GRPO)":[163],"using":[164],"ranking":[166],"rewards,":[167],"further":[169],"integrate":[170],"GRPO":[171],"into":[172],"training":[175],"core":[180],"signal":[182],"generation.":[185],"Extensive":[186],"experiments":[187],"show":[188],"that":[189,201],"our":[190],"framework":[191],"achieves":[192],"substantially":[193],"better":[194],"alignment":[195],"judgments,":[198],"producing":[199],"preserve":[202],"while":[205],"enhancing":[206],"Code,":[212],"models,":[213],"datasets":[215],"will":[216],"be":[217],"released":[218],"upon":[219],"paper":[220],"acceptance.":[221]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-27T00:00:00"}