{"id":"https://openalex.org/W2033250440","doi":"https://doi.org/10.1109/tip.2013.2291328","title":"Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree","display_name":"Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree","publication_year":2013,"publication_date":"2013-11-19","ids":{"openalex":"https://openalex.org/W2033250440","doi":"https://doi.org/10.1109/tip.2013.2291328","mag":"2033250440","pmid":"https://pubmed.ncbi.nlm.nih.gov/26270908"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2013.2291328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2013.2291328","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/A5079937813","display_name":"Linchao Bao","orcid":"https://orcid.org/0000-0001-9543-3754"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Linchao Bao","raw_affiliation_strings":["Department of Computer Science, University of Hong Kong, Hong Kong, China","Department of Computer Science,  University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Computer Science,  University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068034476","display_name":"Yibing Song","orcid":"https://orcid.org/0000-0003-3667-531X"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yibing Song","raw_affiliation_strings":["Department of Computer Science, University of Hong Kong, Hong Kong, China","Department of Computer Science,  University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Computer Science,  University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034454494","display_name":"Qingxiong Yang","orcid":"https://orcid.org/0000-0002-4378-2335"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Qingxiong Yang","raw_affiliation_strings":["Department of Computer Science, University of Hong Kong, Hong Kong, China","Department of Computer Science,  University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Computer Science,  University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100619854","display_name":"Hao Yuan","orcid":"https://orcid.org/0009-0008-2179-8556"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Yuan","raw_affiliation_strings":["BOPU Technologies, Shenzhen, China","BOPU Technol., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BOPU Technologies, Shenzhen, China","institution_ids":[]},{"raw_affiliation_string":"BOPU Technol., Shenzhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100367358","display_name":"Gang Wang","orcid":"https://orcid.org/0000-0002-2288-3807"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Gang Wang","raw_affiliation_strings":["Nanyang Technological University, Advanced Digital Science Center, Singapore","Adv. Digital Sci. Center, Nanyang Technol. Univ., Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Advanced Digital Science Center, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Adv. Digital Sci. Center, Nanyang Technol. Univ., Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.4319,"has_fulltext":false,"cited_by_count":131,"citation_normalized_percentile":{"value":0.9666199,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"23","issue":"2","first_page":"555","last_page":"569"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":1.0,"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":1.0,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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":0.9988999962806702,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6327476501464844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5390050411224365},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5277101397514343},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5169355273246765},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.488398015499115},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4628125727176666},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4590332508087158},{"id":"https://openalex.org/keywords/minimum-spanning-tree","display_name":"Minimum spanning tree","score":0.4385177195072174},{"id":"https://openalex.org/keywords/kernel-adaptive-filter","display_name":"Kernel adaptive filter","score":0.42210447788238525},{"id":"https://openalex.org/keywords/bilateral-filter","display_name":"Bilateral filter","score":0.4177912473678589},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35346418619155884},{"id":"https://openalex.org/keywords/filter-design","display_name":"Filter design","score":0.27094942331314087}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6327476501464844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5390050411224365},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5277101397514343},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5169355273246765},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.488398015499115},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4628125727176666},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4590332508087158},{"id":"https://openalex.org/C13743678","wikidata":"https://www.wikidata.org/wiki/Q240464","display_name":"Minimum spanning tree","level":2,"score":0.4385177195072174},{"id":"https://openalex.org/C13107197","wikidata":"https://www.wikidata.org/wiki/Q6394192","display_name":"Kernel adaptive filter","level":4,"score":0.42210447788238525},{"id":"https://openalex.org/C156140930","wikidata":"https://www.wikidata.org/wiki/Q860417","display_name":"Bilateral filter","level":3,"score":0.4177912473678589},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35346418619155884},{"id":"https://openalex.org/C22597639","wikidata":"https://www.wikidata.org/wiki/Q5449227","display_name":"Filter design","level":3,"score":0.27094942331314087},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2013.2291328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2013.2291328","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:26270908","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26270908","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},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/3a511059-a165-489b-9037-035b65645c14","is_oa":false,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/3a511059-a165-489b-9037-035b65645c14","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bao , L , Song , Y , Yang , Q , Yuan , H & Wang , G 2014 , ' Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree ' , IEEE Transactions on Image Processing , vol. 23 , no. 2 , 6665130 , pp. 555-569 . https://doi.org/10.1109/TIP.2013.2291328","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W32035173","https://openalex.org/W1562826660","https://openalex.org/W1579811087","https://openalex.org/W1873172440","https://openalex.org/W1964772475","https://openalex.org/W1967795753","https://openalex.org/W1979369422","https://openalex.org/W1987844198","https://openalex.org/W1993465480","https://openalex.org/W1995194116","https://openalex.org/W1995858705","https://openalex.org/W1997701791","https://openalex.org/W1999478155","https://openalex.org/W2017927472","https://openalex.org/W2019904315","https://openalex.org/W2032965166","https://openalex.org/W2039580825","https://openalex.org/W2040334833","https://openalex.org/W2042281163","https://openalex.org/W2056370875","https://openalex.org/W2063572802","https://openalex.org/W2065533005","https://openalex.org/W2078718577","https://openalex.org/W2084224084","https://openalex.org/W2088230067","https://openalex.org/W2096768337","https://openalex.org/W2099244020","https://openalex.org/W2121927366","https://openalex.org/W2123407276","https://openalex.org/W2126043703","https://openalex.org/W2137203830","https://openalex.org/W2141957843","https://openalex.org/W2143414773","https://openalex.org/W2144531167","https://openalex.org/W2150134853","https://openalex.org/W2155479981","https://openalex.org/W2160451035","https://openalex.org/W2160956336","https://openalex.org/W2162618331","https://openalex.org/W2244684328","https://openalex.org/W2752885492","https://openalex.org/W2996868574","https://openalex.org/W3138063419","https://openalex.org/W3139167831","https://openalex.org/W4239395373","https://openalex.org/W4247811648","https://openalex.org/W6660133353","https://openalex.org/W6666562274","https://openalex.org/W6678827375","https://openalex.org/W6681426593"],"related_works":["https://openalex.org/W2391378893","https://openalex.org/W3088540525","https://openalex.org/W4379881777","https://openalex.org/W1484352354","https://openalex.org/W2319170675","https://openalex.org/W2153173504","https://openalex.org/W2744626287","https://openalex.org/W3046800873","https://openalex.org/W2574820257","https://openalex.org/W1981838057"],"abstract_inverted_index":{"We":[0,161],"present":[1],"a":[2,34,46,70,183,202,230],"new":[3],"efficient":[4,165],"edge-preserving":[5],"filter-\"tree":[6],"filter\"-to":[7],"achieve":[8],"strong":[9],"image":[10,86,107,194,205],"smoothing.":[11],"The":[12,79,142,178,223],"proposed":[13,224],"filter":[14,32,94,144,182,226],"can":[15,145,200],"smooth":[16,99],"out":[17,100,156],"high-contrast":[18],"details":[19,103],"while":[20,104,126],"preserving":[21,105],"major":[22,106],"edges,":[23],"which":[24,154],"is":[25,33,39,62,227],"not":[26],"achievable":[27],"for":[28],"bilateral-filter-like":[29],"techniques.":[30],"Tree":[31],"weighted-average":[35],"filter,":[36],"whose":[37],"kernel":[38],"derived":[40],"by":[41,64],"viewing":[42],"pixel":[43,51],"affinity":[44],"in":[45,69,111,186],"probabilistic":[47],"framework":[48],"simultaneously":[49],"considering":[50],"spatial":[52],"distance,":[53],"color/intensity":[54],"difference,":[55],"as":[56,58,67],"well":[57],"connectedness.":[59],"Pixel":[60],"connectedness":[61],"acquired":[63],"treating":[65],"pixels":[66,87,110,122,127,138],"nodes":[68],"minimum":[71],"spanning":[72],"tree":[73,91,143,181,225],"(MST)":[74],"extracted":[75],"from":[76,137],"the":[77,90,93,96,124,140,174,219],"image.":[78],"fact":[80],"that":[81],"an":[82,164,211],"MST":[83,168],"makes":[84],"all":[85],"connected":[88,118],"through":[89,123],"endues":[92],"with":[95],"power":[97],"to":[98,119,157,171,191],"high-contrast,":[101],"fine-scale":[102],"structures,":[108],"since":[109],"small":[112],"isolated":[113],"region":[114,131],"will":[115,132],"be":[116,133,146],"closely":[117],"surrounding":[120],"majority":[121],"tree,":[125],"inside":[128],"large":[129],"homogeneous":[130],"automatically":[134],"dragged":[135],"away":[136],"outside":[139],"region.":[141],"separated":[147],"into":[148],"two":[149],"other":[150],"filters,":[151],"both":[152],"of":[153,193,221,232],"turn":[155],"have":[158],"fast":[159,197],"algorithms.":[160],"also":[162],"propose":[163],"linear":[166],"time":[167],"extraction":[169],"algorithm":[170],"further":[172],"improve":[173],"whole":[175],"filtering":[176],"speed.":[177],"algorithms":[179],"give":[180],"great":[184],"advantage":[185],"low":[187],"computational":[188],"complexity":[189],"(linear":[190],"number":[192],"pixels)":[195],"and":[196],"speed:":[198],"it":[199],"process":[201],"1-megapixel":[203],"8-bit":[204],"at":[206],"~":[207],"0.25":[208],"s":[209],"on":[210,229],"Intel":[212],"3.4":[213],"GHz":[214],"Core":[215],"i7":[216],"CPU":[217],"(including":[218],"construction":[220],"MST).":[222],"demonstrated":[228],"variety":[231],"applications.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":19},{"year":2017,"cited_by_count":21},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":4}],"updated_date":"2026-07-17T09:13:05.818461","created_date":"2025-10-10T00:00:00"}