{"id":"https://openalex.org/W4386598572","doi":"https://doi.org/10.1109/icip49359.2023.10222672","title":"Epigraphically-Relaxed Linearly-Involved Generalized Moreau-Enhanced Model for Layered Mixed Norm Regularization","display_name":"Epigraphically-Relaxed Linearly-Involved Generalized Moreau-Enhanced Model for Layered Mixed Norm Regularization","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386598572","doi":"https://doi.org/10.1109/icip49359.2023.10222672"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5092844088","display_name":"Akari Katsuma","orcid":null},"institutions":[{"id":"https://openalex.org/I116465919","display_name":"Kogakuin University","ror":"https://ror.org/01wc2tq75","country_code":"JP","type":"education","lineage":["https://openalex.org/I116465919"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akari Katsuma","raw_affiliation_strings":["Kogakuin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kogakuin University","institution_ids":["https://openalex.org/I116465919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070866771","display_name":"Seisuke Kyochi","orcid":"https://orcid.org/0000-0002-2383-0176"},"institutions":[{"id":"https://openalex.org/I116465919","display_name":"Kogakuin University","ror":"https://ror.org/01wc2tq75","country_code":"JP","type":"education","lineage":["https://openalex.org/I116465919"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Seisuke Kyochi","raw_affiliation_strings":["Kogakuin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kogakuin University","institution_ids":["https://openalex.org/I116465919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040341698","display_name":"Shunsuke Ono","orcid":"https://orcid.org/0000-0001-7890-5131"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shunsuke Ono","raw_affiliation_strings":["Tokyo Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058321875","display_name":"Ivan Selesnick","orcid":"https://orcid.org/0000-0002-4939-3971"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ivan Selesnick","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3739,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66633191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2240","last_page":"2244"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10727","display_name":"Ultrasound Imaging and Elastography","score":0.9984999895095825,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.644968569278717},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.6225034594535828},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6029343008995056},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.5613399744033813},{"id":"https://openalex.org/keywords/convexity","display_name":"Convexity","score":0.5366432666778564},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.44217538833618164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43551087379455566},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4211123585700989},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.41927242279052734},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4105125665664673},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38938623666763306},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1920156478881836},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10731616616249084}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.644968569278717},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.6225034594535828},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6029343008995056},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.5613399744033813},{"id":"https://openalex.org/C72134830","wikidata":"https://www.wikidata.org/wiki/Q5166524","display_name":"Convexity","level":2,"score":0.5366432666778564},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.44217538833618164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43551087379455566},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4211123585700989},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.41927242279052734},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4105125665664673},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38938623666763306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1920156478881836},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10731616616249084},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10222672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2763136412","display_name":null,"funder_award_id":"22H03610","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5480500786","display_name":"Prior learning by deep epigraphical network and its application to rare imaging","funder_award_id":"21K04045","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6222945185","display_name":null,"funder_award_id":"JPMJPR21C4","funder_id":"https://openalex.org/F4320338111","funder_display_name":"Precursory Research for Embryonic Science and Technology"},{"id":"https://openalex.org/G6844658531","display_name":"\u5206\u6563\u30c7\u30fc\u30bf\u30ea\u30b6\u30ec\u30af\u30b7\u30e7\u30f3\uff1a\u67d4\u8edf\u6027\u3068\u52b9\u7387\u6027\u3092\u517c\u306d\u5099\u3048\u305f\u8a08\u6e2c\u60c5\u5831\u518d\u69cb\u6210\u6280\u8853\u306e\u5275\u51fa","funder_award_id":"23K24866","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320338111","display_name":"Precursory Research for Embryonic Science and Technology","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W368469426","https://openalex.org/W1587448943","https://openalex.org/W1895161527","https://openalex.org/W1963865224","https://openalex.org/W1986312779","https://openalex.org/W1998991750","https://openalex.org/W2000594266","https://openalex.org/W2014823423","https://openalex.org/W2060331468","https://openalex.org/W2082473969","https://openalex.org/W2092663520","https://openalex.org/W2103559027","https://openalex.org/W2114423093","https://openalex.org/W2117406282","https://openalex.org/W2121927366","https://openalex.org/W2123031198","https://openalex.org/W2132382511","https://openalex.org/W2142058898","https://openalex.org/W2396831964","https://openalex.org/W2594827121","https://openalex.org/W2620850570","https://openalex.org/W2763081248","https://openalex.org/W2939946185","https://openalex.org/W2982087216","https://openalex.org/W3015297507","https://openalex.org/W3031397607","https://openalex.org/W3102889930","https://openalex.org/W3103883263","https://openalex.org/W3128266486","https://openalex.org/W3209953137","https://openalex.org/W4205213118","https://openalex.org/W4210665207","https://openalex.org/W4229650096","https://openalex.org/W4285242719","https://openalex.org/W4361984142"],"related_works":["https://openalex.org/W1964872188","https://openalex.org/W2187391117","https://openalex.org/W1981809986","https://openalex.org/W2949367465","https://openalex.org/W2587048717","https://openalex.org/W4235203617","https://openalex.org/W2006359264","https://openalex.org/W3029201158","https://openalex.org/W2158224665","https://openalex.org/W2208932071"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"an":[3],"epigraphically-relaxed":[4],"linearly-involved":[5],"generalized":[6],"Moreau-enhanced":[7],"(ER-LiGME)":[8],"model":[9,140],"for":[10],"layered":[11,23],"mixed":[12,24,62,146],"norm":[13,63,70,100,147],"regularization.":[14,101],"Group":[15],"sparse":[16],"and":[17,36,71,113,149,155],"low-rank":[18],"(GSpLr)-aware":[19],"modeling":[20,48,86],"using":[21],"\u21131/nuclear-norm-based":[22],"norms":[25],"has":[26],"succeeded":[27],"in":[28,153],"precise":[29],"high":[30],"dimensional":[31],"signal":[32],"recovery,":[33],"e.g.,":[34],"images":[35],"videos.":[37],"Our":[38],"previous":[39],"work":[40],"significantly":[41],"expands":[42],"the":[43,46,92,97,134,138],"potential":[44],"of":[45],"GSpLr-aware":[47],"by":[49,65],"epigraphical":[50,73],"relaxation":[51],"(ER).":[52],"It":[53],"enables":[54],"us":[55],"to":[56,96,118,141],"handle":[57],"a":[58,69],"(even":[59],"non-proximable)":[60,145],"deeply-layered":[61,143],"minimization":[64],"decoupling":[66],"it":[67,89],"into":[68],"multiple":[72],"constraints":[74],"(if":[75],"each":[76],"proximity":[77],"operator":[78],"is":[79,87],"available).":[80],"One":[81],"problem":[82],"with":[83,137],"typical":[84],"SpLr":[85],"that":[88],"suffers":[90],"from":[91],"underestimation":[93],"effect":[94],"due":[95],"\u21131and":[98],"nuclear":[99],"To":[102],"circumvent":[103],"this":[104,130],"problem,":[105],"LiGME":[106,139],"penalty":[107],"functions,":[108],"which":[109],"modify":[110],"conventional":[111],"sparsity":[112],"low-rankness":[114],"promoting":[115],"convex":[116],"functions":[117],"nonconvex":[119],"ones":[120],"while":[121],"keeping":[122],"overall":[123],"convexity,":[124],"have":[125],"been":[126],"proposed":[127],"conventionally.":[128],"In":[129],"work,":[131],"we":[132],"integrate":[133],"ER":[135],"technique":[136],"realize":[142],"(possibly":[144],"regularization":[148],"show":[150],"its":[151],"effectiveness":[152],"denoising":[154],"compressed":[156],"sensing":[157],"reconstruction.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}