{"id":"https://openalex.org/W7160941822","doi":"https://doi.org/10.48550/arxiv.2605.09628","title":"DegBins: Degradation-Driven Binning for Depth Super-Resolution","display_name":"DegBins: Degradation-Driven Binning for Depth Super-Resolution","publication_year":2026,"publication_date":"2026-05-10","ids":{"openalex":"https://openalex.org/W7160941822","doi":"https://doi.org/10.48550/arxiv.2605.09628"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09628","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.2605.09628","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135998037","display_name":"Zhiqiang Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Zhiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135973081","display_name":"Zhengxue Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhengxue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135981891","display_name":"Jian Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135964605","display_name":"Gim Hee Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Gim Hee","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/T10531","display_name":"Advanced Vision and Imaging","score":0.8669999837875366,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.8669999837875366,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.05350000038743019,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.012900000438094139,"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/bin","display_name":"Bin","score":0.7788000106811523},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.762499988079071},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6653000116348267},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5411999821662903},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5055999755859375},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49129998683929443},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.40540000796318054}],"concepts":[{"id":"https://openalex.org/C156273044","wikidata":"https://www.wikidata.org/wiki/Q4913766","display_name":"Bin","level":2,"score":0.7788000106811523},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.762499988079071},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6653000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5834000110626221},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5540000200271606},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5411999821662903},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5055999755859375},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.484499990940094},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3919000029563904},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37290000915527344},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09628","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.2605.09628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09628","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":[{"score":0.6281712055206299,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Depth":[0],"super-resolution":[1],"(DSR)":[2],"aims":[3],"to":[4,44,73],"recover":[5],"a":[6,34,65,85,96,126,150],"high-resolution":[7],"(HR)":[8],"depth":[9,92,101,175],"map":[10],"from":[11],"its":[12],"low-resolution":[13],"(LR)":[14],"counterpart.":[15],"With":[16],"color":[17],"image":[18],"guidance,":[19],"this":[20,39,60],"task":[21],"is":[22,42,93],"typically":[23],"formulated":[24],"as":[25,84,95],"learning":[26],"the":[27,47,81,90,118,166],"residual":[28,76,91],"between":[29,50,121],"HR":[30,51,122],"and":[31,52,112,123,135,161,205],"LR":[32,124],"in":[33,125,178,200],"low-dimensional":[35],"feature":[36,128],"space.":[37],"However,":[38],"additive":[40],"formulation":[41],"insufficient":[43],"accurately":[45],"capture":[46],"complex":[48,184],"relationship":[49,120],"LR,":[53],"especially":[54],"under":[55],"spatially":[56],"varying":[57],"degradations.":[58],"In":[59],"paper,":[61],"we":[62],"introduce":[63],"DegBins,":[64],"novel":[66],"DSR":[67,83],"framework":[68],"that":[69,193],"leverages":[70],"degradation-driven":[71],"binning":[72],"adaptively":[74],"enhance":[75],"modeling.":[77],"Specifically,":[78],"DegBins":[79,116,148,194],"reformulates":[80],"regression-based":[82],"hybrid":[86],"classification-regression":[87],"problem,":[88],"where":[89,154],"represented":[94],"linear":[97],"combination":[98],"of":[99,202],"discrete":[100],"bins":[102],"weighted":[103],"by":[104],"their":[105],"learned":[106],"probability":[107,136,162],"distribution,":[108],"yielding":[109],"more":[110,173],"flexible":[111],"expressive":[113],"representations.":[114],"Furthermore,":[115],"models":[117],"degradation":[119,141],"high-dimensional":[127],"space,":[129],"enabling":[130],"adaptive":[131],"bin":[132,159],"range":[133],"adjustment":[134],"optimization":[137],"conditioned":[138],"on":[139,165],"local":[140],"characteristics.":[142],"To":[143],"progressively":[144],"improve":[145],"reconstruction":[146],"quality,":[147],"adopts":[149],"multi-stage":[151],"refinement":[152],"scheme,":[153],"each":[155],"stage":[156],"performs":[157],"finer-grained":[158],"partitioning":[160],"updating":[163],"based":[164],"former":[167],"estimation.":[168],"This":[169],"coarse-to-fine":[170],"design":[171],"facilitates":[172],"accurate":[174],"recovery,":[176],"particularly":[177],"regions":[179],"with":[180],"severe":[181],"degradations":[182],"or":[183],"structural":[185],"variations.":[186],"Extensive":[187],"experiments":[188],"across":[189],"five":[190],"benchmarks":[191],"demonstrate":[192],"consistently":[195],"outperforms":[196],"existing":[197],"state-of-the-art":[198],"methods":[199],"terms":[201],"accuracy,":[203],"robustness,":[204],"generalization.":[206]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}