{"id":"https://openalex.org/W3133082282","doi":"https://doi.org/10.1117/12.2580434","title":"TextureWGAN: texture preserving WGAN with MLE regularizer for inverse problems","display_name":"TextureWGAN: texture preserving WGAN with MLE regularizer for inverse problems","publication_year":2021,"publication_date":"2021-02-13","ids":{"openalex":"https://openalex.org/W3133082282","doi":"https://doi.org/10.1117/12.2580434","mag":"3133082282"},"language":"en","primary_location":{"id":"doi:10.1117/12.2580434","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2580434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Image Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5018707544","display_name":"Masaki Ikuta","orcid":"https://orcid.org/0000-0002-6503-5007"},"institutions":[{"id":"https://openalex.org/I43579087","display_name":"University of Wisconsin\u2013Milwaukee","ror":"https://ror.org/031q21x57","country_code":"US","type":"education","lineage":["https://openalex.org/I43579087"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Masaki Ikuta","raw_affiliation_strings":["Univ. of Wisconsin-Milwaukee (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Wisconsin-Milwaukee (United States)","institution_ids":["https://openalex.org/I43579087"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100433189","display_name":"Jun Zhang","orcid":"https://orcid.org/0000-0002-6666-8703"},"institutions":[{"id":"https://openalex.org/I43579087","display_name":"University of Wisconsin\u2013Milwaukee","ror":"https://ror.org/031q21x57","country_code":"US","type":"education","lineage":["https://openalex.org/I43579087"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Zhang","raw_affiliation_strings":["Univ. of Wisconsin-Milwaukee (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Wisconsin-Milwaukee (United States)","institution_ids":["https://openalex.org/I43579087"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5737,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65930631,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"37","last_page":"37"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11183","display_name":"Advanced X-ray Imaging Techniques","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9872000217437744,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7110655903816223},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6725883483886719},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6125075221061707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5950934886932373},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5564889907836914},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.5545569062232971},{"id":"https://openalex.org/keywords/peak-signal-to-noise-ratio","display_name":"Peak signal-to-noise ratio","score":0.5227627754211426},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.513969361782074},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4857838451862335},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.47730815410614014},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47549355030059814},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.46142011880874634},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4346187710762024},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36874526739120483},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3291509747505188},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0944671630859375}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7110655903816223},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6725883483886719},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6125075221061707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5950934886932373},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5564889907836914},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.5545569062232971},{"id":"https://openalex.org/C154579607","wikidata":"https://www.wikidata.org/wiki/Q3373850","display_name":"Peak signal-to-noise ratio","level":3,"score":0.5227627754211426},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.513969361782074},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4857838451862335},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.47730815410614014},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47549355030059814},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.46142011880874634},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4346187710762024},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36874526739120483},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3291509747505188},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0944671630859375},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2580434","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2580434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2102148524","https://openalex.org/W2314720829","https://openalex.org/W4385074335","https://openalex.org/W2626189183","https://openalex.org/W2626268514","https://openalex.org/W2315873829","https://openalex.org/W2883512610","https://openalex.org/W2895464841","https://openalex.org/W3082444306","https://openalex.org/W142830878"],"abstract_inverted_index":{"Many":[0],"algorithms":[1],"and":[2,20,31,55,85,91,147,166,181],"methods":[3,74],"have":[4],"been":[5,48],"proposed":[6,26,107,173],"for":[7,78,116,155],"inverse":[8,117],"problems":[9],"particularly":[10],"with":[11,40],"the":[12,28,35,68,93,97,122,151,172,179,182],"recent":[13],"surge":[14],"of":[15,70,96],"interest":[16],"in":[17,51],"machine":[18],"learning":[19,22],"deep":[21],"methods.":[23],"Among":[24],"all":[25,79],"methods,":[27],"most":[29,152],"popular":[30],"effective":[32,50,126],"method":[33,46,60,110,124,174],"is":[34,61,150],"convolutional":[36],"neural":[37],"network":[38],"(CNN)":[39],"mean":[41],"square":[42],"error":[43],"(MSE).":[44],"This":[45],"has":[47],"proven":[49],"super-resolution,":[52],"image":[53,56,84,88,101,129,145,185,190],"de-noising,":[54],"reconstruction.":[57],"However,":[58],"this":[59,104],"known":[62],"to":[63,67,127,140,162,170,188],"over-smooth":[64],"images":[65],"due":[66],"nature":[69],"MSE.":[71],"MSE":[72],"based":[73,111],"minimize":[75],"Euclidean":[76],"distance":[77],"pixels":[80,98],"between":[81],"a":[82,86,108,134],"baseline":[83],"generated":[87],"by":[89],"CNN":[90],"ignore":[92],"spatial":[94],"information":[95],"such":[99],"as":[100],"texture.":[102,130,191],"In":[103],"paper,":[105],"we":[106],"new":[109],"on":[112],"Wasserstein":[113],"GAN":[114],"(WGAN)":[115],"problems.":[118],"We":[119,158,176],"showed":[120],"that":[121],"WGAN-based":[123],"was":[125],"preserve":[128,141],"It":[131],"also":[132,177],"used":[133,159],"maximum":[135],"likelihood":[136],"estimation":[137],"(MLE)":[138],"regularizer":[139],"pixel":[142,148],"fidelity.":[143],"Maintaining":[144],"texture":[146,186],"fidelity":[149],"important":[153],"requirement":[154],"medical":[156],"imaging.":[157],"Peak":[160],"Signal":[161],"Noise":[163],"Ratio":[164],"(PSNR)":[165],"Structure":[167],"Similarity":[168],"(SSIM)":[169],"evaluate":[171],"quantitatively.":[175],"conducted":[178],"first-order":[180],"second-order":[183],"statistical":[184],"analysis":[187],"assess":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
