{"id":"https://openalex.org/W4213425554","doi":"https://doi.org/10.1109/tmi.2022.3154011","title":"Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks","display_name":"Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks","publication_year":2022,"publication_date":"2022-02-24","ids":{"openalex":"https://openalex.org/W4213425554","doi":"https://doi.org/10.1109/tmi.2022.3154011","pmid":"https://pubmed.ncbi.nlm.nih.gov/35201984"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2022.3154011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3154011","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.tue.nl/ws/files/214336074/Noise_Reduction_in_CT_Using_Learned_Wavelet_Frame_Shrinkage_Networks_published.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034708744","display_name":"Luis A. Zavala-Mondrag\u00f3n","orcid":"https://orcid.org/0000-0002-4469-8362"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Luis A. Zavala-Mondragon","raw_affiliation_strings":["Electrical Engineering Department, VCA Laboratory, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-4469-8362","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, VCA Laboratory, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002344694","display_name":"Peter Rongen","orcid":"https://orcid.org/0000-0002-8322-7467"},"institutions":[{"id":"https://openalex.org/I4210122849","display_name":"Philips (Netherlands)","ror":"https://ror.org/02p2bgp27","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210122849"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Peter Rongen","raw_affiliation_strings":["Philips Medical Systems Nederland B.V., Best, PC, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-8322-7467","affiliations":[{"raw_affiliation_string":"Philips Medical Systems Nederland B.V., Best, PC, The Netherlands","institution_ids":["https://openalex.org/I4210122849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111985276","display_name":"Javier Oliv\u00e1n Besc\u00f3s","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122849","display_name":"Philips (Netherlands)","ror":"https://ror.org/02p2bgp27","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210122849"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Javier Olivan Bescos","raw_affiliation_strings":["Philips Medical Systems Nederland B.V., Best, PC, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Philips Medical Systems Nederland B.V., Best, PC, The Netherlands","institution_ids":["https://openalex.org/I4210122849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029008213","display_name":"Peter H. N. de With","orcid":"https://orcid.org/0000-0002-7639-7716"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Peter H. N. de With","raw_affiliation_strings":["Electrical Engineering Department, VCA Laboratory, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-7639-7716","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, VCA Laboratory, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075677746","display_name":"Fons van der Sommen","orcid":"https://orcid.org/0000-0002-3593-2356"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Fons van der Sommen","raw_affiliation_strings":["Electrical Engineering Department, VCA Laboratory, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-3593-2356","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, VCA Laboratory, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7261,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.8532998,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"41","issue":"8","first_page":"2048","last_page":"2066"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"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":0.9995999932289124,"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/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9976999759674072,"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/noise-reduction","display_name":"Noise reduction","score":0.6512911915779114},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6313421726226807},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6262931823730469},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5960302352905273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5836898684501648},{"id":"https://openalex.org/keywords/convolutional-code","display_name":"Convolutional code","score":0.5760722756385803},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5639462471008301},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.516137957572937},{"id":"https://openalex.org/keywords/shrinkage","display_name":"Shrinkage","score":0.5130337476730347},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4801344573497772},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4779503345489502},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4586314260959625},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44951558113098145},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4375113248825073},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.41770657896995544},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3985022306442261},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24167916178703308},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1388174593448639},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09545141458511353}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6512911915779114},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6313421726226807},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6262931823730469},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5960302352905273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5836898684501648},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.5760722756385803},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5639462471008301},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.516137957572937},{"id":"https://openalex.org/C180145272","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage","level":2,"score":0.5130337476730347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4801344573497772},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4779503345489502},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4586314260959625},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44951558113098145},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4375113248825073},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.41770657896995544},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3985022306442261},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24167916178703308},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1388174593448639},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09545141458511353},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059629","descriptor_name":"Signal-To-Noise Ratio","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.1109/tmi.2022.3154011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3154011","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:35201984","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35201984","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":null},{"id":"pmh:oai:pure.tue.nl:openaire/b694b8f0-3b2d-4e7c-8071-789258d25591","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/b694b8f0-3b2d-4e7c-8071-789258d25591","pdf_url":"https://pure.tue.nl/ws/files/214336074/Noise_Reduction_in_CT_Using_Learned_Wavelet_Frame_Shrinkage_Networks_published.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Zavala Mondragon, L A, Rongen, P M J, Oliv\u00e1n Besc\u00f3s, J, de With, P H N & van der Sommen, F 2022, 'Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks', IEEE Transactions on Medical Imaging, vol. 41, no. 8, 9721076, pp. 2048-2066. https://doi.org/10.1109/TMI.2022.3154011","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.tue.nl:publications/b694b8f0-3b2d-4e7c-8071-789258d25591","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85125305377&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Zavala Mondragon, L A, Rongen, P M J, Oliv\u00e1n Besc\u00f3s, J, de With, P H N & van der Sommen, F 2022, 'Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks', IEEE Transactions on Medical Imaging, vol. 41, no. 8, 9721076, pp. 2048-2066. https://doi.org/10.1109/TMI.2022.3154011","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:tue:oai:pure.tue.nl:publications/b694b8f0-3b2d-4e7c-8071-789258d25591","is_oa":true,"landing_page_url":"https://research.tue.nl/nl/publications/b694b8f0-3b2d-4e7c-8071-789258d25591","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Medical Imaging, 41(8):9721076, 2048 - 2066. Institute of Electrical and Electronics Engineers","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.tue.nl:openaire/b694b8f0-3b2d-4e7c-8071-789258d25591","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/b694b8f0-3b2d-4e7c-8071-789258d25591","pdf_url":"https://pure.tue.nl/ws/files/214336074/Noise_Reduction_in_CT_Using_Learned_Wavelet_Frame_Shrinkage_Networks_published.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Zavala Mondragon, L A, Rongen, P M J, Oliv\u00e1n Besc\u00f3s, J, de With, P H N & van der Sommen, F 2022, 'Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks', IEEE Transactions on Medical Imaging, vol. 41, no. 8, 9721076, pp. 2048-2066. https://doi.org/10.1109/TMI.2022.3154011","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8299999833106995}],"awards":[{"id":"https://openalex.org/G8941051391","display_name":null,"funder_award_id":"780026 \u201cNEXIS.\u201d","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320306143","display_name":"Mayo Clinic","ror":"https://ror.org/02qp3tb03"},{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4213425554.pdf","grobid_xml":"https://content.openalex.org/works/W4213425554.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W107726186","https://openalex.org/W1901129140","https://openalex.org/W2007203285","https://openalex.org/W2055750192","https://openalex.org/W2083927153","https://openalex.org/W2107099926","https://openalex.org/W2115706991","https://openalex.org/W2121775913","https://openalex.org/W2125527601","https://openalex.org/W2133665775","https://openalex.org/W2140742952","https://openalex.org/W2145876651","https://openalex.org/W2183341477","https://openalex.org/W2184045051","https://openalex.org/W2476315418","https://openalex.org/W2481798478","https://openalex.org/W2508457857","https://openalex.org/W2567077865","https://openalex.org/W2574952845","https://openalex.org/W2584483805","https://openalex.org/W2619810135","https://openalex.org/W2771101929","https://openalex.org/W2777802649","https://openalex.org/W2795777276","https://openalex.org/W2798746587","https://openalex.org/W2963392702","https://openalex.org/W2963494934","https://openalex.org/W2964188306","https://openalex.org/W2971503239","https://openalex.org/W2979294594","https://openalex.org/W2989045104","https://openalex.org/W2990138404","https://openalex.org/W3004829055","https://openalex.org/W3020881381","https://openalex.org/W3046819731","https://openalex.org/W3047238636","https://openalex.org/W3047700752","https://openalex.org/W3094438190","https://openalex.org/W3103645830","https://openalex.org/W3124596402","https://openalex.org/W3152340680","https://openalex.org/W3216826802","https://openalex.org/W4214806317","https://openalex.org/W6686677765","https://openalex.org/W6738581260","https://openalex.org/W6759151774","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2391958761","https://openalex.org/W2765453142","https://openalex.org/W2356780078","https://openalex.org/W2356008845","https://openalex.org/W255134961","https://openalex.org/W2348314720","https://openalex.org/W2243547089","https://openalex.org/W2316482937","https://openalex.org/W2253986335","https://openalex.org/W2979071418"],"abstract_inverted_index":{"Encoding-decoding":[0],"(ED)":[1],"CNNs":[2,53,76,90],"have":[3],"demonstrated":[4],"state-of-the-art":[5,174],"performance":[6,171],"for":[7],"noise":[8],"reduction":[9],"over":[10],"the":[11,17,22,32,48,63,80,105,116,122,126,130,136,147,154,178],"past":[12],"years.":[13],"This":[14],"has":[15,29,149],"triggered":[16],"pursuit":[18],"of":[19,25,34,89,121,139,153,158],"better":[20],"understanding":[21],"inner":[23],"workings":[24],"such":[26,176],"architectures,":[27],"which":[28],"led":[30],"to":[31,66,173],"theory":[33],"deep":[35],"convolutional":[36],"framelets":[37],"(TDCF),":[38],"revealing":[39],"important":[40],"links":[41],"between":[42],"signal":[43],"processing":[44],"and":[45,93,112,143,183],"CNNs.":[46],"Specifically,":[47],"TDCF":[49],"demonstrates":[50],"that":[51,77,85],"ReLU":[52],"induce":[54],"low-rankness,":[55],"since":[56],"these":[57,87],"models":[58],"often":[59],"do":[60,78],"not":[61],"satisfy":[62],"necessary":[64],"redundancy":[65],"achieve":[67],"perfect":[68],"reconstruction":[69],"(PR).":[70],"In":[71,145],"contrast,":[72],"this":[73],"paper":[74],"explores":[75],"meet":[79],"PR":[81,94,127],"conditions.":[82],"We":[83],"demonstrate":[84],"in":[86,185],"type":[88],"soft":[91],"shrinkage":[92,108,131],"can":[95],"be":[96],"assumed.":[97],"Furthermore,":[98],"based":[99,134],"on":[100,135],"our":[101],"explorations":[102],"we":[103],"propose":[104],"learned":[106],"wavelet-frame":[107],"network,":[109],"or":[110],"LWFSN":[111,148],"its":[113],"residual":[114],"counterpart,":[115],"rLWFSN.":[117],"The":[118],"ED":[119],"path":[120],"(r)LWFSN":[123],"complies":[124],"with":[125],"conditions,":[128],"while":[129,168],"stage":[132],"is":[133],"linear":[137],"expansion":[138],"thresholds":[140],"proposed":[141],"Blu":[142],"Luisier.":[144],"addition,":[146],"only":[150],"a":[151],"fraction":[152],"training":[155],"parameters":[156],"(<1%)":[157],"conventional":[159],"CNNs,":[160],"very":[161],"small":[162],"inference":[163],"times,":[164],"low":[165],"memory":[166],"footprint,":[167],"still":[169],"achieving":[170],"close":[172],"alternatives,":[175],"as":[177],"tight":[179],"frame":[180],"(TF)":[181],"U-Net":[182],"FBPConvNet,":[184],"low-dose":[186],"CT":[187],"denoising.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}