{"id":"https://openalex.org/W2416874002","doi":"https://doi.org/10.1109/acpr.2015.7486610","title":"On coupled regularization for non-convex variational image enhancement","display_name":"On coupled regularization for non-convex variational image enhancement","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2416874002","doi":"https://doi.org/10.1109/acpr.2015.7486610","mag":"2416874002"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5053645636","display_name":"Freddie \u00c5str\u00f6m","orcid":"https://orcid.org/0000-0001-6441-5609"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Freddie Astrom","raw_affiliation_strings":["Heidelberg Collaboratory for Image Processing, Heidelberg University, Germany"],"affiliations":[{"raw_affiliation_string":"Heidelberg Collaboratory for Image Processing, Heidelberg University, Germany","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088815590","display_name":"Christoph Schn\u00f6rr","orcid":"https://orcid.org/0000-0002-8999-2338"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Schnorr","raw_affiliation_strings":["Image and Pattern Analysis Group, Heidelberg University, Germany"],"affiliations":[{"raw_affiliation_string":"Image and Pattern Analysis Group, Heidelberg University, Germany","institution_ids":["https://openalex.org/I223822909"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053645636"],"corresponding_institution_ids":["https://openalex.org/I223822909"],"apc_list":null,"apc_paid":null,"fwci":1.2995,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.80430015,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"786","last_page":"790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9980999827384949,"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":0.9933000206947327,"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/proper-convex-function","display_name":"Proper convex function","score":0.6655616760253906},{"id":"https://openalex.org/keywords/convex-analysis","display_name":"Convex analysis","score":0.663201093673706},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.5802844762802124},{"id":"https://openalex.org/keywords/proximal-gradient-methods-for-learning","display_name":"Proximal gradient methods for learning","score":0.5787739157676697},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.567293643951416},{"id":"https://openalex.org/keywords/convex-function","display_name":"Convex function","score":0.5411327481269836},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5168512463569641},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.5157113671302795},{"id":"https://openalex.org/keywords/subderivative","display_name":"Subderivative","score":0.5074047446250916},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.48369699716567993},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.45660191774368286},{"id":"https://openalex.org/keywords/convex-combination","display_name":"Convex combination","score":0.42836955189704895},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32303279638290405},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3222835063934326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21157410740852356},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.13853609561920166},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.06413638591766357}],"concepts":[{"id":"https://openalex.org/C50862404","wikidata":"https://www.wikidata.org/wiki/Q3075259","display_name":"Proper convex function","level":5,"score":0.6655616760253906},{"id":"https://openalex.org/C12108790","wikidata":"https://www.wikidata.org/wiki/Q2234833","display_name":"Convex analysis","level":4,"score":0.663201093673706},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.5802844762802124},{"id":"https://openalex.org/C79248915","wikidata":"https://www.wikidata.org/wiki/Q17086776","display_name":"Proximal gradient methods for learning","level":5,"score":0.5787739157676697},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.567293643951416},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.5411327481269836},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5168512463569641},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.5157113671302795},{"id":"https://openalex.org/C200661725","wikidata":"https://www.wikidata.org/wiki/Q289500","display_name":"Subderivative","level":4,"score":0.5074047446250916},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.48369699716567993},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.45660191774368286},{"id":"https://openalex.org/C111110010","wikidata":"https://www.wikidata.org/wiki/Q2627315","display_name":"Convex combination","level":4,"score":0.42836955189704895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32303279638290405},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3222835063934326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21157410740852356},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.13853609561920166},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.06413638591766357}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8799999952316284,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W269003556","https://openalex.org/W1525535255","https://openalex.org/W1914015964","https://openalex.org/W1965392255","https://openalex.org/W2022735534","https://openalex.org/W2029460515","https://openalex.org/W2040657720","https://openalex.org/W2049984505","https://openalex.org/W2061052400","https://openalex.org/W2103559027","https://openalex.org/W2110158442","https://openalex.org/W2133665775","https://openalex.org/W2142058898","https://openalex.org/W2164278908","https://openalex.org/W2296319761","https://openalex.org/W2952652977","https://openalex.org/W2996868574","https://openalex.org/W4206180546","https://openalex.org/W4250589301","https://openalex.org/W4292363360","https://openalex.org/W6631362934"],"related_works":["https://openalex.org/W2026600343","https://openalex.org/W4382519084","https://openalex.org/W2356261519","https://openalex.org/W2478309485","https://openalex.org/W2040676043","https://openalex.org/W165488893","https://openalex.org/W1510770274","https://openalex.org/W4361807553","https://openalex.org/W1978014154","https://openalex.org/W1974461145"],"abstract_inverted_index":{"A":[0],"natural":[1],"continuation":[2],"from":[3,25],"conventional":[4],"convex":[5,26,41,65],"methods":[6],"for":[7,100],"image":[8],"enhancement":[9],"is":[10,64,75],"the":[11,76,83,92,97,101,127],"transition":[12],"to":[13,28,72],"non-convex":[14,18,35,68,114],"formulations.":[15],"However,":[16],"strictly":[17],"models":[19],"do":[20],"not":[21],"admit":[22],"traditional":[23],"tools":[24],"optimization":[27],"be":[29],"used.":[30],"To":[31],"resolve":[32],"this":[33,51],"drawback,":[34],"problems":[36],"are":[37,123],"often":[38],"cast":[39],"into":[40],"formulations":[42],"by":[43],"relaxing":[44],"stringent":[45],"assumptions":[46],"on":[47],"model":[48],"properties.":[49],"In":[50,105],"work":[52],"we":[53,109],"present":[54],"an":[55,61],"alternative":[56],"approach.":[57],"We":[58,95],"study":[59,110],"when":[60],"energy":[62],"functional":[63],"given":[66],"a":[67,79,87,106,111],"penalty":[69],"term.":[70,94],"Key":[71],"our":[73],"formulation":[74],"introduction":[77],"of":[78,113],"novel":[80],"coupling":[81],"between":[82],"discretization":[84],"scheme":[85],"and":[86],"non-local":[88,98],"weight":[89],"function":[90],"in":[91],"data":[93],"interpret":[96],"weights":[99],"finite":[102],"difference":[103],"operators.":[104],"denoising":[107],"application":[108],"class":[112],"\u2113":[115],"<sub":[116],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[117],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">p</sub>":[118],"-norms.":[119],"The":[120],"resulting":[121],"energies":[122],"globally":[124],"minimized":[125],"using":[126],"popular":[128],"ADMM.":[129]},"counts_by_year":[{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}