{"id":"https://openalex.org/W7128729597","doi":"https://doi.org/10.1109/access.2026.3664078","title":"Preserving Fine-Grained Droplet Structures by Spatial Gaussian Blurring Against Pooling Deformation in U-Net-Based Semantic Segmentation","display_name":"Preserving Fine-Grained Droplet Structures by Spatial Gaussian Blurring Against Pooling Deformation in U-Net-Based Semantic Segmentation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7128729597","doi":"https://doi.org/10.1109/access.2026.3664078"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3664078","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664078","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3664078","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000388666","display_name":"Wei Lun Lim","orcid":"https://orcid.org/0000-0002-0301-2846"},"institutions":[{"id":"https://openalex.org/I84339108","display_name":"Sunway University","ror":"https://ror.org/04mjt7f73","country_code":"MY","type":"education","lineage":["https://openalex.org/I84339108"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Wei Lun Lim","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-0301-2846","affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, Malaysia","institution_ids":["https://openalex.org/I84339108"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119715201","display_name":"Matthew Y.W. Teow","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092355","display_name":"Academy of Medicine","ror":"https://ror.org/00fnk0q46","country_code":"SG","type":"education","lineage":["https://openalex.org/I4210092355"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Matthew Y. W. Teow","raw_affiliation_strings":["PSB Academy, University Partnership (Coventry University), Marina Square, Singapore"],"raw_orcid":"https://orcid.org/0009-0001-7777-2960","affiliations":[{"raw_affiliation_string":"PSB Academy, University Partnership (Coventry University), Marina Square, Singapore","institution_ids":["https://openalex.org/I4210092355"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013462633","display_name":"Richard T.K. Wong","orcid":"https://orcid.org/0000-0002-4480-5076"},"institutions":[{"id":"https://openalex.org/I84339108","display_name":"Sunway University","ror":"https://ror.org/04mjt7f73","country_code":"MY","type":"education","lineage":["https://openalex.org/I84339108"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Richard T. K. Wong","raw_affiliation_strings":["School of Engineering, Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-4480-5076","affiliations":[{"raw_affiliation_string":"School of Engineering, Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, Malaysia","institution_ids":["https://openalex.org/I84339108"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002606307","display_name":"Sian Lun Lau","orcid":"https://orcid.org/0000-0002-7709-7774"},"institutions":[{"id":"https://openalex.org/I84339108","display_name":"Sunway University","ror":"https://ror.org/04mjt7f73","country_code":"MY","type":"education","lineage":["https://openalex.org/I84339108"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Sian Lun Lau","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-7709-7774","affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, Malaysia","institution_ids":["https://openalex.org/I84339108"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14375097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"25354","last_page":"25369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.321399986743927,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.321399986743927,"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/T11019","display_name":"Image Enhancement Techniques","score":0.15489999949932098,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.06949999928474426,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/pooling","display_name":"Pooling","score":0.788100004196167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6147000193595886},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.571399986743927},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5044999718666077},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5002999901771545},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49900001287460327},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4885999858379364},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4643000066280365},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4383000135421753}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.788100004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6891999840736389},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6699000000953674},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6147000193595886},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.571399986743927},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5044999718666077},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5002999901771545},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4643000066280365},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4383000135421753},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.414900004863739},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.337799996137619},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C29168087","wikidata":"https://www.wikidata.org/wiki/Q1026711","display_name":"Blob detection","level":5,"score":0.30869999527931213},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.302700012922287},{"id":"https://openalex.org/C73313986","wikidata":"https://www.wikidata.org/wiki/Q355386","display_name":"Luminance","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.29649999737739563},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.289900004863739},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.2531999945640564}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3664078","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664078","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:cd243f8891ba4ea796b2c2f21ba32506","is_oa":false,"landing_page_url":"https://doaj.org/article/cd243f8891ba4ea796b2c2f21ba32506","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 25354-25369 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3664078","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664078","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6942232847213745,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Pooling":[0],"operations":[1],"in":[2,34,60,190],"convolutional":[3],"neural":[4],"networks":[5],"often":[6],"lead":[7],"to":[8,71,89,98,112,202,217],"the":[9,48,61,63,100,108,140,151,163,203,214,221],"loss":[10,40],"of":[11,43,142,162],"fine-grained":[12,52,114,196],"object":[13],"features,":[14],"particularly":[15],"when":[16],"those":[17],"objects":[18],"occupy":[19],"only":[20],"a":[21,82,104,135,143,186],"few":[22],"pixels":[23],"(e.g.,":[24],"5":[25,27],"\u00d7":[26],"or":[28],"smaller).":[29],"This":[30,167],"problem":[31],"is":[32,148,213,228],"critical":[33],"droplet":[35,45,53,73,115],"analysis,":[36],"where":[37],"pooling-induced":[38],"feature":[39,131,153,165,180],"causes":[41],"deformation":[42],"finegrained":[44],"boundaries":[46],"within":[47],"encoded":[49],"representation.":[50],"When":[51],"features":[54,176],"are":[55,198],"structurally":[56],"deformed":[57],"and":[58,69,121,177],"vanished":[59],"encoder,":[62],"resulting":[64],"ineffective":[65],"representation":[66],"impairs":[67,77],"learning":[68,110],"leads":[70],"inaccurate":[72],"segmentation,":[74],"which":[75],"severely":[76],"downstream":[78],"decision-making.":[79],"We":[80],"propose":[81],"data":[83],"augmentation":[84,212],"strategy":[85],"using":[86],"Gaussian":[87,210],"blurring":[88,96],"mitigate":[90],"this":[91],"problem.":[92],"By":[93],"applying":[94],"spatial":[95,130,179],"prior":[97],"encoding,":[99],"blur":[101,211],"acts":[102],"as":[103],"contour":[105],"regularization,":[106],"strengthening":[107],"network":[109],"capability":[111],"retain":[113],"structure":[116],"through":[117],"subsequent":[118],"pooling":[119,125],"layers":[120],"improving":[122],"robustness":[123],"against":[124],"deformation.":[126],"To":[127],"further":[128],"strengthen":[129],"interaction,":[132],"we":[133],"integrate":[134],"graph":[136,147,170],"convolution":[137],"module":[138],"at":[139],"bottleneck":[141,152],"U-Net":[144],"architecture.":[145],"The":[146,226],"constructed":[149],"from":[150],"map,":[154],"with":[155],"edges":[156],"weighted":[157],"by":[158],"raw":[159],"intensity":[160],"values":[161],"corresponding":[164],"elements.":[166],"non-parametric,":[168],"intensity-based":[169],"formulation":[171],"captures":[172],"structural":[173],"relationships":[174],"among":[175],"complements":[178],"preservation.":[181],"Our":[182],"combined":[183],"method":[184],"achieves":[185],"16.5%":[187],"relative":[188],"improvement":[189],"Recall":[191],"(R),":[192],"indicating":[193],"that":[194,209],"more":[195],"droplets":[197],"correctly":[199],"segmented":[200],"compared":[201],"baseline":[204],"U-Net.":[205],"Ablation":[206],"studies":[207],"confirm":[208],"dominant":[215],"contributor":[216],"these":[218],"gains,":[219],"while":[220],"GraphSAGE":[222],"provides":[223],"complementary":[224],"refinement.":[225],"code":[227],"available":[229],"at:":[230],"https://github.com/lynerlwl/blur-unet-segmentation.":[231]},"counts_by_year":[],"updated_date":"2026-02-21T06:11:54.161237","created_date":"2026-02-13T00:00:00"}
