{"id":"https://openalex.org/W4400912682","doi":"https://doi.org/10.3390/rs16152692","title":"DDSR: Degradation-Aware Diffusion Model for Spectral Reconstruction from RGB Images","display_name":"DDSR: Degradation-Aware Diffusion Model for Spectral Reconstruction from RGB Images","publication_year":2024,"publication_date":"2024-07-23","ids":{"openalex":"https://openalex.org/W4400912682","doi":"https://doi.org/10.3390/rs16152692"},"language":"en","primary_location":{"id":"doi:10.3390/rs16152692","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16152692","pdf_url":"https://www.mdpi.com/2072-4292/16/15/2692/pdf?version=1721729036","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/16/15/2692/pdf?version=1721729036","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113300833","display_name":"Yunlai Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunlai Chen","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068068982","display_name":"Xiaoyan Zhang","orcid":"https://orcid.org/0000-0002-5446-7568"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyan Zhang","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068068982"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.7722,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71869853,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"16","issue":"15","first_page":"2692","last_page":"2692"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994000196456909,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994000196456909,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9987000226974487,"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/T11666","display_name":"Color Science and Applications","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.47843578457832336},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46907466650009155},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.4405454099178314},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.41560816764831543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3395664393901825},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.3223041296005249},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2419571578502655},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05884936451911926},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.04439619183540344}],"concepts":[{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.47843578457832336},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46907466650009155},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.4405454099178314},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.41560816764831543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3395664393901825},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.3223041296005249},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2419571578502655},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05884936451911926},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.04439619183540344},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16152692","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16152692","pdf_url":"https://www.mdpi.com/2072-4292/16/15/2692/pdf?version=1721729036","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:915e90491a04483f80866f1c61f3cfad","is_oa":true,"landing_page_url":"https://doaj.org/article/915e90491a04483f80866f1c61f3cfad","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 16, Iss 15, p 2692 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16152692","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16152692","pdf_url":"https://www.mdpi.com/2072-4292/16/15/2692/pdf?version=1721729036","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4400912682.pdf"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W8423413","https://openalex.org/W2100109944","https://openalex.org/W2129069237","https://openalex.org/W2129353435","https://openalex.org/W2140196014","https://openalex.org/W2520430674","https://openalex.org/W2776639132","https://openalex.org/W2808122338","https://openalex.org/W2893739000","https://openalex.org/W2895021180","https://openalex.org/W3035556176","https://openalex.org/W3035598238","https://openalex.org/W3096739052","https://openalex.org/W3097353710","https://openalex.org/W3105109286","https://openalex.org/W3107358227","https://openalex.org/W3155072588","https://openalex.org/W3193940263","https://openalex.org/W3201949031","https://openalex.org/W3209671871","https://openalex.org/W3212228063","https://openalex.org/W4224294196","https://openalex.org/W4283736883","https://openalex.org/W4285085921","https://openalex.org/W4292793979","https://openalex.org/W4292794818","https://openalex.org/W4310631370","https://openalex.org/W4312628443","https://openalex.org/W4313467048","https://openalex.org/W4383818954","https://openalex.org/W4388461678","https://openalex.org/W4389610328","https://openalex.org/W4390338480","https://openalex.org/W4390597559","https://openalex.org/W4390871757","https://openalex.org/W4390873572","https://openalex.org/W4392709969","https://openalex.org/W4394755170","https://openalex.org/W4395038484","https://openalex.org/W4396769343","https://openalex.org/W4399206013","https://openalex.org/W6779823529","https://openalex.org/W6794269193","https://openalex.org/W6858045014"],"related_works":["https://openalex.org/W4246450666","https://openalex.org/W4388998267","https://openalex.org/W2898370298","https://openalex.org/W2137437058","https://openalex.org/W4396520874","https://openalex.org/W4390401159","https://openalex.org/W2744391499","https://openalex.org/W4230250635","https://openalex.org/W3041790586","https://openalex.org/W2018879842"],"abstract_inverted_index":{"The":[0,104],"reconstruction":[1],"of":[2,27,63,86,94,116,128],"hyperspectral":[3,16],"images":[4,8,29],"(HSIs)":[5],"from":[6,107],"RGB":[7,28,110],"is":[9,111,155],"an":[10,24,148],"attractive":[11],"low-cost":[12],"approach":[13],"to":[14,41,45,109,123,142,157],"recover":[15],"information.":[17],"However,":[18],"existing":[19],"approaches":[20],"focus":[21],"on":[22,52,163],"learning":[23],"end-to-end":[25,87],"mapping":[26],"and":[30,174],"their":[31],"corresponding":[32],"HSIs":[33],"with":[34,54],"neural":[35],"networks,":[36],"which":[37,120,154],"makes":[38],"it":[39],"difficult":[40],"ensure":[42],"generalization":[43,84,178],"due":[44],"the":[46,66,82,91,95,125,129,139,143],"fact":[47],"that":[48,168],"they":[49],"are":[50,121],"trained":[51],"data":[53],"a":[55,60,100,114,134,176],"specific":[56],"degradation":[57,105,118,159],"process.":[58],"As":[59],"new":[61],"paradigm":[62],"generative":[64],"models,":[65],"diffusion":[67,96,102,130,144],"model":[68,131],"has":[69],"shown":[70],"great":[71],"potential":[72],"in":[73,77],"image":[74],"restoration,":[75],"especially":[76],"noisy":[78],"contexts.":[79],"To":[80],"address":[81],"unstable":[83],"ability":[85,93],"models":[88],"while":[89],"exploiting":[90],"powerful":[92],"model,":[97,145],"we":[98,146],"propose":[99],"degradation-aware":[101,135,140],"model.":[103],"process":[106,127],"HSI":[108],"modeled":[112],"as":[113],"combination":[115],"multiple":[117],"operators,":[119],"used":[122],"guide":[124],"inverse":[126],"by":[132],"utilizing":[133],"correction.":[136],"By":[137],"integrating":[138],"correction":[141],"obtain":[147],"efficient":[149],"solver":[150],"for":[151],"spectral":[152],"reconstruction,":[153],"robust":[156],"different":[158],"patterns.":[160],"Experiment":[161],"results":[162],"various":[164],"public":[165],"datasets":[166],"demonstrate":[167],"our":[169],"method":[170],"achieves":[171],"competitive":[172],"performance":[173],"shows":[175],"promising":[177],"ability.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-10-10T00:00:00"}