{"id":"https://openalex.org/W4377079729","doi":"https://doi.org/10.1109/tip.2023.3276330","title":"A Convolutional Neural Network-Based Conditional Random Field Model for Structured Multi-Focus Image Fusion Robust to Noise","display_name":"A Convolutional Neural Network-Based Conditional Random Field Model for Structured Multi-Focus Image Fusion Robust to Noise","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4377079729","doi":"https://doi.org/10.1109/tip.2023.3276330","pmid":"https://pubmed.ncbi.nlm.nih.gov/37200125"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2023.3276330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2023.3276330","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/A5069520395","display_name":"Odysseas Bouzos","orcid":"https://orcid.org/0000-0003-2588-0078"},"institutions":[{"id":"https://openalex.org/I147962203","display_name":"Democritus University of Thrace","ror":"https://ror.org/03bfqnx40","country_code":"GR","type":"education","lineage":["https://openalex.org/I147962203"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Odysseas Bouzos","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece"],"raw_orcid":"https://orcid.org/0000-0003-2588-0078","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece","institution_ids":["https://openalex.org/I147962203"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049683515","display_name":"Ioannis Andreadis","orcid":"https://orcid.org/0000-0003-1703-9633"},"institutions":[{"id":"https://openalex.org/I147962203","display_name":"Democritus University of Thrace","ror":"https://ror.org/03bfqnx40","country_code":"GR","type":"education","lineage":["https://openalex.org/I147962203"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Ioannis Andreadis","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece","institution_ids":["https://openalex.org/I147962203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064706177","display_name":"Nikolaos Mitianoudis","orcid":"https://orcid.org/0000-0003-0898-6102"},"institutions":[{"id":"https://openalex.org/I147962203","display_name":"Democritus University of Thrace","ror":"https://ror.org/03bfqnx40","country_code":"GR","type":"education","lineage":["https://openalex.org/I147962203"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Nikolaos Mitianoudis","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece"],"raw_orcid":"https://orcid.org/0000-0003-0898-6102","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece","institution_ids":["https://openalex.org/I147962203"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I147962203"],"apc_list":null,"apc_paid":null,"fwci":4.3135,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.94876275,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"32","issue":null,"first_page":"2915","last_page":"2930"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9998999834060669,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9994999766349792,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9990000128746033,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7361493110656738},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7311740517616272},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6981759071350098},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.6939225792884827},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5348825454711914},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5086732506752014},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5046416521072388},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42838403582572937},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4273264706134796},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4114440977573395},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3769216537475586},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27806130051612854},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.19551503658294678}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7361493110656738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7311740517616272},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6981759071350098},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.6939225792884827},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5348825454711914},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5086732506752014},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5046416521072388},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42838403582572937},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4273264706134796},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4114440977573395},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3769216537475586},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27806130051612854},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.19551503658294678},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2023.3276330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2023.3276330","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:37200125","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37200125","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1533072162","https://openalex.org/W1934334601","https://openalex.org/W1955055330","https://openalex.org/W1965606870","https://openalex.org/W1970302834","https://openalex.org/W1980382026","https://openalex.org/W1994616697","https://openalex.org/W1997480692","https://openalex.org/W1999034606","https://openalex.org/W2002285735","https://openalex.org/W2010307644","https://openalex.org/W2020442368","https://openalex.org/W2023844059","https://openalex.org/W2025133197","https://openalex.org/W2027694926","https://openalex.org/W2031489346","https://openalex.org/W2039465985","https://openalex.org/W2050159143","https://openalex.org/W2073779945","https://openalex.org/W2082506960","https://openalex.org/W2131168375","https://openalex.org/W2142060261","https://openalex.org/W2143516773","https://openalex.org/W2146353910","https://openalex.org/W2160956336","https://openalex.org/W2168783614","https://openalex.org/W2306859282","https://openalex.org/W2400541731","https://openalex.org/W2522703671","https://openalex.org/W2559870345","https://openalex.org/W2588655935","https://openalex.org/W2742180631","https://openalex.org/W2777967210","https://openalex.org/W2794731943","https://openalex.org/W2798987894","https://openalex.org/W2808236703","https://openalex.org/W2905307044","https://openalex.org/W2941380944","https://openalex.org/W2952327021","https://openalex.org/W2961850738","https://openalex.org/W2963787388","https://openalex.org/W3005577541","https://openalex.org/W3010335025","https://openalex.org/W3040592542","https://openalex.org/W3046194589","https://openalex.org/W3081131687","https://openalex.org/W3088623196","https://openalex.org/W3095064240","https://openalex.org/W3105639468","https://openalex.org/W3127812914","https://openalex.org/W3134036581","https://openalex.org/W3158729725","https://openalex.org/W3162209674","https://openalex.org/W3201708049","https://openalex.org/W3210616569","https://openalex.org/W4283732315","https://openalex.org/W4308979724","https://openalex.org/W6662978298","https://openalex.org/W6774006736","https://openalex.org/W6795426856"],"related_works":["https://openalex.org/W2541791370","https://openalex.org/W2035976912","https://openalex.org/W4385415357","https://openalex.org/W3094187672","https://openalex.org/W2921707373","https://openalex.org/W1803059841","https://openalex.org/W2767651786","https://openalex.org/W2912288872","https://openalex.org/W2891108385","https://openalex.org/W564581980"],"abstract_inverted_index":{"The":[0,80,124],"limited":[1,37],"depth":[2],"of":[3,5,14,41,56,84,92,100,156,205],"field":[4],"optical":[6],"lenses,":[7],"makes":[8],"multi-focus":[9],"image":[10,61,145],"fusion":[11],"(MFIF)":[12],"algorithms":[13],"vital":[15],"importance.":[16],"Lately,":[17],"Convolutional":[18,70],"Neural":[19,71],"Networks":[20],"(CNN)":[21],"have":[22,48],"been":[23],"widely":[24],"adopted":[25],"in":[26,104],"MFIF":[27,57,162,185],"methods,":[28],"however":[29],"their":[30],"predictions":[31],"mostly":[32],"lack":[33],"structure":[34],"and":[35,90,95,115,143,149,177,189],"are":[36,118],"by":[38,120,172],"the":[39,42,54,85,96,101,154,173],"size":[40],"receptive":[43],"field.":[44],"Moreover,":[45],"since":[46],"images":[47],"noise":[49,62,69,170,199],"due":[50],"to":[51,60,68,106,131,152,167,197],"various":[52],"sources,":[53],"development":[55],"methods":[58,186],"robust":[59,67,196],"is":[63,78,129,147,150,164],"required.":[64],"A":[65,137,160],"novel":[66],"Network-based":[72],"Conditional":[73],"Random":[74],"Field":[75],"(mf-CNNCRF)":[76],"model":[77,81],"introduced.":[79],"takes":[82],"advantage":[83],"powerful":[86],"mapping":[87],"between":[88],"input":[89,191],"output":[91],"CNN":[93,122],"networks":[94,155],"long":[97],"range":[98],"interactions":[99],"CRF":[102,158],"models":[103],"order":[105],"reach":[107,132],"structured":[108,133],"inference.":[109],"Rich":[110],"priors":[111],"for":[112,135,187],"both":[113,157],"unary":[114],"smoothness":[116],"terms":[117],"learned":[119],"training":[121],"networks.":[123],"\u03b1":[125],"-expansion":[126],"graph-cut":[127],"algorithm":[128],"used":[130,151],"inference":[134],"MFIF.":[136],"new":[138],"dataset,":[139],"which":[140],"includes":[141],"clean":[142,188],"noisy":[144,190],"pairs,":[146],"introduced":[148,171],"train":[153],"terms.":[159],"low-light":[161],"dataset":[163],"also":[165],"developed":[166],"demonstrate":[168],"real-life":[169],"camera":[174],"sensor.":[175],"Qualitative":[176],"quantitative":[178],"evaluation":[179],"prove":[180],"that":[181],"mf-CNNCRF":[182],"outperforms":[183],"state-of-the-art":[184],"images,":[192],"while":[193],"being":[194],"more":[195],"different":[198],"types":[200],"without":[201],"requiring":[202],"prior":[203],"knowledge":[204],"noise.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}