{"id":"https://openalex.org/W4417051812","doi":"https://doi.org/10.1109/tip.2025.3637687","title":"LPATR-Net: Learnable Piecewise Affine Transformation Regression Assisted Data-Driven Dehazing Framework","display_name":"LPATR-Net: Learnable Piecewise Affine Transformation Regression Assisted Data-Driven Dehazing Framework","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4417051812","doi":"https://doi.org/10.1109/tip.2025.3637687","pmid":"https://pubmed.ncbi.nlm.nih.gov/41348791"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2025.3637687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2025.3637687","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051494158","display_name":"Yuelong Li","orcid":"https://orcid.org/0000-0003-1355-7943"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuelong Li","raw_affiliation_strings":["School of Artificial Intelligence, Tiangong University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-1355-7943","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Tiangong University, Tianjin, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100727322","display_name":"Fei Chen","orcid":"https://orcid.org/0000-0001-9643-7191"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Chen","raw_affiliation_strings":["School of Innovation, Tiangong University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Innovation, Tiangong University, Tianjin, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhenwei Liu","orcid":"https://orcid.org/0009-0008-6959-3912"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenwei Liu","raw_affiliation_strings":["Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0008-6959-3912","affiliations":[{"raw_affiliation_string":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109683227","display_name":"Tianyu Zang","orcid":null},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Zang","raw_affiliation_strings":["School of Software, Tiangong University, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0000-5623-3530","affiliations":[{"raw_affiliation_string":"School of Software, Tiangong University, Tianjin, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048371289","display_name":"Jianming Wang","orcid":"https://orcid.org/0000-0003-2685-4437"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianming Wang","raw_affiliation_strings":["Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-2685-4437","affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China","institution_ids":["https://openalex.org/I198091727"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3546042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"8002","last_page":"8017"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.8672000169754028,"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/T11019","display_name":"Image Enhancement Techniques","score":0.8672000169754028,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.015699999406933784,"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/T10930","display_name":"Flood Risk Assessment and Management","score":0.013899999670684338,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6741999983787537},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.6276999711990356},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5511999726295471},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.531499981880188},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5227000117301941},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.5126000046730042},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4677000045776367},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4068000018596649},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3718000054359436}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6988000273704529},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6741999983787537},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.6276999711990356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6191999912261963},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5511999726295471},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.531499981880188},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5227000117301941},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.5126000046730042},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4185999929904938},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39739999175071716},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C17095337","wikidata":"https://www.wikidata.org/wiki/Q2375229","display_name":"Piecewise linear function","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3197999894618988},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2930999994277954},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2605000138282776},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2025.3637687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2025.3637687","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:41348791","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41348791","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1426701596","display_name":"\u4e00\u822c\u6027\u7269\u4f53\u906e\u6321\u7684\u81ea\u52a8\u8865\u507f\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61771340","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2308111426","display_name":null,"funder_award_id":"23YJCZH258","funder_id":"https://openalex.org/F4320335982","funder_display_name":"Humanities and Social Science Fund of Ministry of Education of China"},{"id":"https://openalex.org/G3547500960","display_name":null,"funder_award_id":"202408120075","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G4553317648","display_name":null,"funder_award_id":"25BC057","funder_id":"https://openalex.org/F4320335869","funder_display_name":"National Social Science Fund of China"},{"id":"https://openalex.org/G6219155711","display_name":null,"funder_award_id":"62471333","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6756322957","display_name":null,"funder_award_id":"25JCYBJC00160","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G7989413580","display_name":null,"funder_award_id":"24JCYBJC00310","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null},{"id":"https://openalex.org/F4320335869","display_name":"National Social Science Fund of China","ror":null},{"id":"https://openalex.org/F4320335982","display_name":"Humanities and Social Science Fund of Ministry of Education of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2034787273","https://openalex.org/W2065002911","https://openalex.org/W2088436396","https://openalex.org/W2107220193","https://openalex.org/W2128254161","https://openalex.org/W2156936307","https://openalex.org/W2467473805","https://openalex.org/W2509770929","https://openalex.org/W2551717155","https://openalex.org/W2735974062","https://openalex.org/W2736707178","https://openalex.org/W2767420414","https://openalex.org/W2779176852","https://openalex.org/W2804102604","https://openalex.org/W2883581904","https://openalex.org/W2962754725","https://openalex.org/W2962782447","https://openalex.org/W2963152299","https://openalex.org/W2963306157","https://openalex.org/W2963928582","https://openalex.org/W2970360808","https://openalex.org/W2985030998","https://openalex.org/W2990007814","https://openalex.org/W2996367318","https://openalex.org/W2998249728","https://openalex.org/W3021850033","https://openalex.org/W3035601380","https://openalex.org/W3089141666","https://openalex.org/W3108002680","https://openalex.org/W3112782334","https://openalex.org/W3133769291","https://openalex.org/W3162681312","https://openalex.org/W3173269149","https://openalex.org/W3173342677","https://openalex.org/W3174407746","https://openalex.org/W3177127346","https://openalex.org/W3180034634","https://openalex.org/W3204515269","https://openalex.org/W3206886340","https://openalex.org/W4205261430","https://openalex.org/W4283819192","https://openalex.org/W4294891478","https://openalex.org/W4312556940","https://openalex.org/W4312617404","https://openalex.org/W4312625691","https://openalex.org/W4312678820","https://openalex.org/W4327808545","https://openalex.org/W4385245566","https://openalex.org/W4385767446","https://openalex.org/W4386076209","https://openalex.org/W4386076504","https://openalex.org/W4388189239","https://openalex.org/W4388430459","https://openalex.org/W4389305847","https://openalex.org/W4390738234","https://openalex.org/W4390872931","https://openalex.org/W4390874447","https://openalex.org/W4391092744","https://openalex.org/W4393150006","https://openalex.org/W4402704549","https://openalex.org/W4403287549","https://openalex.org/W4410852273"],"related_works":[],"abstract_inverted_index":{"Nowadays,":[0],"data-driven":[1,116,262],"learning":[2,17,86],"based":[3],"deep":[4,263],"neural":[5],"network":[6],"(DNN)":[7],"is":[8,35,45,65,92,158,191,225,298],"the":[9,21,50,88,95,143,150,159,188,217,249,282,286],"most":[10,84],"dominant":[11],"SOTA":[12],"image":[13,89],"dehazing":[14,90,163,240],"framework.":[15],"Here,":[16],"to":[18,27,48,146,153,172,227,276],"perfectly":[19],"simulate":[20],"underlying":[22],"mapping":[23],"rules":[24],"(from":[25],"hazy":[26],"clear)":[28],"told":[29],"by":[30],"massive":[31],"paired":[32],"training":[33,108],"data":[34,109],"its":[36,278],"core":[37,288],"driving":[38],"force.":[39],"However,":[40],"under":[41],"genuine":[42],"scenarios,":[43],"it":[44],"extremely":[46],"hard":[47],"guarantee":[49],"100%":[51],"qualification":[52],"of":[53,97,99,134,215,219,221,252,285],"all":[54,260],"collected":[55],"ground":[56,101],"truth":[57,102],"(GT)":[58],"haze-free":[59],"data.":[60],"That's":[61],"because":[62],"natural":[63],"weather":[64],"hardly":[66],"controlled,":[67],"and":[68,80,110,258],"many":[69],"weathers":[70],"are":[71],"actually":[72],"in":[73,130],"a":[74,121,161,235],"chaotic":[75],"status":[76],"existing":[77],"between":[78],"foggy":[79],"fog-free.":[81],"Thus,":[82],"unlike":[83],"supervised":[85],"issues,":[87],"society":[91],"born":[93],"with":[94,126,167,234],"torture":[96],"part":[98],"faulty":[100,176],"no-haze":[103],"samples.":[104],"Therefore,":[105],"totally":[106],"trusting":[107],"solely":[111],"pursuing":[112,136],"more":[113],"fitting":[114,138,151,168,241],"powerful":[115],"model":[117],"may":[118],"not":[119,181],"be":[120,228],"wise":[122],"solution.":[123],"To":[124],"cope":[125],"this":[127,131],"thorny":[128],"challenge,":[129],"paper,":[132],"instead":[133],"faithfully":[135],"for":[137,213],"capacity":[139],"promotion,":[140],"we":[141],"on":[142,270],"contrary":[144],"choose":[145],"intentionally":[147],"cut":[148],"down":[149],"flexibility":[152],"achieve":[154],"higher-level":[155],"robustness.":[156],"That":[157],"LPATR-Net,":[160],"novel":[162],"framework":[164],"specially":[165],"armed":[166],"power":[169],"suppression":[170],"mechanism":[171],"resist":[173],"intrinsic":[174],"annoying":[175],"GT.":[177],"This":[178],"solution":[179],"does":[180],"involve":[182],"any":[183],"extra":[184],"manually":[185],"labeling.":[186],"Specifically,":[187],"LPATR-Net":[189,246],"architecture":[190],"created":[192],"completely":[193],"around":[194],"elaborately":[195],"designed":[196],"fitting-restrained":[197],"learnable":[198],"piecewise":[199],"affine":[200],"transformation":[201],"regression.":[202],"Since":[203],"such":[204],"low-order":[205],"linear":[206],"regression":[207,257,289],"structure":[208,290],"genetically":[209],"can":[210],"only":[211],"fit":[212],"majority":[214,254],"data,":[216],"interference":[218],"minority":[220],"unqualified":[222],"GT":[223],"samples":[224],"expected":[226],"effectively":[229],"suppressed.":[230],"Through":[231],"further":[232],"coupled":[233],"highly":[236],"customized":[237],"multi-concerns":[238],"high-accuracy":[239],"companion":[242],"component,":[243],"All-Mattering,":[244],"proposed":[245,287],"elegantly":[247],"achieves":[248],"seamless":[250],"integration":[251],"traditional":[253],"determining":[255],"fixed-form":[256],"modern":[259],"freedom":[261],"learning.":[264],"Extensive":[265],"experiments":[266],"have":[267],"been":[268,293],"conducted":[269],"five":[271],"commonly":[272],"utilized":[273],"public":[274],"datasets":[275],"verify":[277],"effectiveness.":[279],"In":[280],"addition,":[281],"wide-range":[283],"transplantability":[284],"has":[291],"also":[292],"experimentally":[294],"confirmed.":[295],"Source":[296],"code":[297],"available":[299],"at":[300],"https://github.com/FeiChen829/LPATR-Net.":[301]},"counts_by_year":[],"updated_date":"2025-12-16T23:43:54.943958","created_date":"2025-12-05T00:00:00"}
