{"id":"https://openalex.org/W2981164480","doi":"https://doi.org/10.1109/icassp40776.2020.9054644","title":"Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising","display_name":"Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W2981164480","doi":"https://doi.org/10.1109/icassp40776.2020.9054644","mag":"2981164480"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9054644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1910.09234","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051997496","display_name":"Tobias Alt","orcid":"https://orcid.org/0000-0002-4451-9512"},"institutions":[{"id":"https://openalex.org/I91712215","display_name":"Saarland University","ror":"https://ror.org/01jdpyv68","country_code":"DE","type":"education","lineage":["https://openalex.org/I91712215"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Tobias Alt","raw_affiliation_strings":["Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Campus E1.7, Saarland University, Saarb\u00fccken, Germany","Saarland Univ,"],"affiliations":[{"raw_affiliation_string":"Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Campus E1.7, Saarland University, Saarb\u00fccken, Germany","institution_ids":["https://openalex.org/I91712215"]},{"raw_affiliation_string":"Saarland Univ,","institution_ids":["https://openalex.org/I91712215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007086183","display_name":"Joachim Weickert","orcid":"https://orcid.org/0000-0002-8494-0045"},"institutions":[{"id":"https://openalex.org/I91712215","display_name":"Saarland University","ror":"https://ror.org/01jdpyv68","country_code":"DE","type":"education","lineage":["https://openalex.org/I91712215"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Joachim Weickert","raw_affiliation_strings":["Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Campus E1.7, Saarland University, Saarb\u00fccken, Germany","Saarland Univ,"],"affiliations":[{"raw_affiliation_string":"Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Campus E1.7, Saarland University, Saarb\u00fccken, Germany","institution_ids":["https://openalex.org/I91712215"]},{"raw_affiliation_string":"Saarland Univ,","institution_ids":["https://openalex.org/I91712215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051997496"],"corresponding_institution_ids":["https://openalex.org/I91712215"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00527171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"2018","last_page":"2022"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9728000164031982,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9649999737739563,"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/shrinkage","display_name":"Shrinkage","score":0.9064894914627075},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.7914172410964966},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7221903800964355},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6782297492027283},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.562286376953125},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5001349449157715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49775388836860657},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48026323318481445},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.45205917954444885},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43368443846702576},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42454129457473755},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4229786992073059},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4189395010471344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15814438462257385},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10419344902038574}],"concepts":[{"id":"https://openalex.org/C180145272","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage","level":2,"score":0.9064894914627075},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.7914172410964966},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7221903800964355},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6782297492027283},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.562286376953125},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5001349449157715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49775388836860657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48026323318481445},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.45205917954444885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43368443846702576},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42454129457473755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4229786992073059},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4189395010471344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15814438462257385},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10419344902038574},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/icassp40776.2020.9054644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1910.09234","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.09234","pdf_url":"https://arxiv.org/pdf/1910.09234","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"mag:2981164480","is_oa":true,"landing_page_url":"https://arxiv.org/abs/1910.09234","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1910.09234","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1910.09234","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"},{"id":"doi:10.17023/5ryp-4w97","is_oa":true,"landing_page_url":"https://doi.org/10.17023/5ryp-4w97","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1910.09234","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.09234","pdf_url":"https://arxiv.org/pdf/1910.09234","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2739469001","display_name":"Inpainting-based Compression of Visual Data","funder_award_id":"741215","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4956428346","display_name":null,"funder_award_id":"Horizon 2020 research and innovatio","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8051717526","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8318064016","display_name":null,"funder_award_id":"Horizon","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2981164480.pdf","grobid_xml":"https://content.openalex.org/works/W2981164480.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W59771946","https://openalex.org/W1507906818","https://openalex.org/W1906770428","https://openalex.org/W2015080174","https://openalex.org/W2051434435","https://openalex.org/W2082076175","https://openalex.org/W2099691301","https://openalex.org/W2108944658","https://openalex.org/W2110158442","https://openalex.org/W2113945798","https://openalex.org/W2115755118","https://openalex.org/W2116692144","https://openalex.org/W2125527601","https://openalex.org/W2130975789","https://openalex.org/W2131686571","https://openalex.org/W2133251749","https://openalex.org/W2146842127","https://openalex.org/W2150134853","https://openalex.org/W2158940042","https://openalex.org/W2164798790","https://openalex.org/W2793441242","https://openalex.org/W2892662896","https://openalex.org/W4214806317","https://openalex.org/W6671185795","https://openalex.org/W6684483400"],"related_works":["https://openalex.org/W2068082650","https://openalex.org/W3145161579","https://openalex.org/W111430742","https://openalex.org/W2045210749","https://openalex.org/W2517377276","https://openalex.org/W1964322372","https://openalex.org/W2766706740","https://openalex.org/W2610878161","https://openalex.org/W2188221237","https://openalex.org/W2901247417","https://openalex.org/W2131676517","https://openalex.org/W2582799169","https://openalex.org/W961744533","https://openalex.org/W2326734190","https://openalex.org/W1775729916","https://openalex.org/W3005226751","https://openalex.org/W2182690387","https://openalex.org/W2185526446","https://openalex.org/W3122296637","https://openalex.org/W2092273390"],"abstract_inverted_index":{"The":[0],"rise":[1],"of":[2,67],"machine":[3],"learning":[4],"in":[5],"image":[6,103],"processing":[7],"has":[8],"created":[9],"a":[10,39,68,118],"gap":[11],"between":[12],"trainable":[13],"data-driven":[14],"and":[15,84],"classical":[16,27,114],"model-":[17],"driven":[18],"approaches:":[19],"While":[20],"learning-based":[21],"models":[22],"often":[23,30],"show":[24,110],"superior":[25],"performance,":[26],"ones":[28],"are":[29],"more":[31],"transparent.":[32],"To":[33],"reduce":[34],"this":[35],"gap,":[36],"we":[37],"introduce":[38],"generic":[40],"wavelet":[41,52,107],"shrinkage":[42,80,96,115],"function":[43,71,81],"for":[44],"denoising":[45],"which":[46,72],"is":[47,62,73,82,99],"adaptive":[48],"to":[49,93,101],"both":[50],"the":[51,57],"scales":[53],"as":[54,56],"well":[55],"noise":[58],"standard":[59],"deviation.":[60],"It":[61],"inferred":[63],"from":[64,75],"trained":[65],"results":[66],"tightly":[69],"parametrised":[70],"inherited":[74],"nonlinear":[76],"diffusion.":[77],"Our":[78],"proposed":[79],"smooth":[83],"compact":[85],"while":[86],"only":[87],"using":[88],"two":[89],"parameters.":[90],"In":[91],"contrast":[92],"many":[94],"existing":[95],"functions,":[97],"it":[98,112],"able":[100],"enhance":[102],"structures":[104],"by":[105,117],"amplifying":[106],"coefficients.":[108],"Experiments":[109],"that":[111],"outperforms":[113],"functions":[116],"significant":[119],"margin.":[120]},"counts_by_year":[],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}
