Noise Learning-Based Denoising Autoencoder
This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE i...
Saved in:
| Published in: | IEEE communications letters Vol. 25; no. 9; pp. 2983 - 2987 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1089-7798, 1558-2558, 1558-2558 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE. |
|---|---|
| AbstractList | This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE. |
| Author | Sung, Ki Won Lee, Woong-Hee Challita, Ursula Ozger, Mustafa |
| Author_xml | – sequence: 1 givenname: Woong-Hee orcidid: 0000-0002-1064-5123 surname: Lee fullname: Lee, Woong-Hee email: woongheelee@korea.ac.kr organization: Department of Control and Instrumentation Engineering, Korea University, Sejong-si, Republic of Korea – sequence: 2 givenname: Mustafa orcidid: 0000-0001-8517-7996 surname: Ozger fullname: Ozger, Mustafa email: ozger@kth.se organization: School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden – sequence: 3 givenname: Ursula orcidid: 0000-0002-6625-1529 surname: Challita fullname: Challita, Ursula email: ursula.challita@ericsson.com organization: Ericsson Research, Stockholm, Sweden – sequence: 4 givenname: Ki Won orcidid: 0000-0001-7642-3067 surname: Sung fullname: Sung, Ki Won email: sungkw@kth.se organization: School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302636$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan) |
| BookMark | eNp9kD1PwzAQhi1UJNrCH4ClEhtSin1u4mQsLV9SShdgtRznUlxKXOxEiH-PS6oODCz3pfe5O70D0qttjYScMzpmjGbX-Wy5WIyBAhtzmrGU0iPSZ3GcRhBCL9Q0zSIhsvSEDLxfU0pTiFmfXD1Z43GUo3K1qVfRjfJYjuZYh3HoR9O2sVhrW6I7JceV2ng82-chebm7fZ49RPny_nE2zSPNhWiiTLGKJYVGCJlDypjWEwUlJlWIXAkNFdNVEU9ACywUV0kpMi2KgCsogA9J1O31X7htC7l15kO5b2mVkXPzOpXWreR78yY5hYQnQX_Z6bfOfrboG7m2ravDixJiwQBYIlhQQafSznrvsDrsZVTuPJS_Hsqdh3LvYYDSP5A2jWqMrRunzOZ_9KJDDSIebmWTBFKe8R8N7YCi |
| CODEN | ICLEF6 |
| CitedBy_id | crossref_primary_10_1088_1742_6596_2822_1_012084 crossref_primary_10_3390_s24227257 crossref_primary_10_1109_LCOMM_2023_3272972 crossref_primary_10_1109_LCOMM_2025_3578081 crossref_primary_10_3390_app13063534 crossref_primary_10_3390_pr9081266 crossref_primary_10_1109_ACCESS_2022_3164714 crossref_primary_10_1177_10775463251342614 crossref_primary_10_1109_TNS_2024_3454701 crossref_primary_10_1007_s11277_022_09693_z crossref_primary_10_1109_ACCESS_2024_3366899 crossref_primary_10_1016_j_oceaneng_2024_117280 crossref_primary_10_1007_s11277_022_09965_8 crossref_primary_10_1109_ACCESS_2025_3529853 crossref_primary_10_1109_TCCN_2022_3195511 crossref_primary_10_3389_fpls_2023_1158933 crossref_primary_10_1080_03772063_2025_2547999 crossref_primary_10_1109_TIM_2025_3545981 crossref_primary_10_3390_electronics13061029 crossref_primary_10_1016_j_compbiomed_2023_107440 crossref_primary_10_1109_ACCESS_2025_3583069 crossref_primary_10_1109_TNSRE_2024_3450854 crossref_primary_10_1109_TIM_2022_3212551 crossref_primary_10_1016_j_medengphy_2025_104406 crossref_primary_10_1002_itl2_70022 crossref_primary_10_3390_app132312734 crossref_primary_10_3390_app15052656 crossref_primary_10_1109_LCOMM_2024_3362509 crossref_primary_10_1109_TVT_2023_3348803 crossref_primary_10_1109_ACCESS_2024_3519580 crossref_primary_10_1016_j_joes_2023_12_004 crossref_primary_10_1007_s00034_025_03262_y crossref_primary_10_1007_s11277_024_10912_y crossref_primary_10_1007_s40766_025_00073_4 crossref_primary_10_3390_s22031260 crossref_primary_10_1016_j_adhoc_2024_103412 crossref_primary_10_1109_TCE_2025_3562388 crossref_primary_10_1109_ACCESS_2023_3323038 crossref_primary_10_1038_s41467_024_54704_1 crossref_primary_10_3390_sym14020395 crossref_primary_10_3390_smartcities8050153 |
| Cites_doi | 10.1109/COMST.2019.2924243 10.1145/1390156.1390294 10.1109/TBC.2002.804034 10.1109/COMST.2019.2926625 10.1109/MCOM.001.1900664 10.1109/MCOM.2019.1800610 10.1109/MSP.2015.2398954 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SP 8FD L7M ADTPV AOWAS D8V |
| DOI | 10.1109/LCOMM.2021.3091800 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace SwePub SwePub Articles SWEPUB Kungliga Tekniska Högskolan |
| DatabaseTitle | CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-2558 |
| EndPage | 2987 |
| ExternalDocumentID | oai_DiVA_org_kth_302636 10_1109_LCOMM_2021_3091800 9462839 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF) funderid: 10.13039/501100003725 – fundername: European Union Horizon 2020 Research and Innovation Programme under the EU/KR PriMO-5G project grantid: 815191 funderid: 10.13039/501100000780 – fundername: Korea University Grant funderid: 10.13039/501100002642 |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV AZLTO BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD ESBDL HZ~ H~9 IES IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TN5 VH1 AAYXX CITATION 7SP 8FD L7M ADTPV AOWAS D8V |
| ID | FETCH-LOGICAL-c377t-9a1f16bce21f132811cc4a2de6fa2d3a7c2f1cfb542c7eba3a6d79c7bc37a2b23 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 49 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000694697800046&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1089-7798 1558-2558 |
| IngestDate | Tue Nov 04 17:20:17 EST 2025 Mon Jun 30 10:12:13 EDT 2025 Sat Nov 29 03:56:07 EST 2025 Tue Nov 18 21:33:22 EST 2025 Wed Aug 27 02:27:34 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c377t-9a1f16bce21f132811cc4a2de6fa2d3a7c2f1cfb542c7eba3a6d79c7bc37a2b23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-7642-3067 0000-0002-1064-5123 0000-0002-6625-1529 0000-0001-8517-7996 |
| OpenAccessLink | https://ieeexplore.ieee.org/document/9462839 |
| PQID | 2571221671 |
| PQPubID | 85419 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_9462839 swepub_primary_oai_DiVA_org_kth_302636 proquest_journals_2571221671 crossref_primary_10_1109_LCOMM_2021_3091800 crossref_citationtrail_10_1109_LCOMM_2021_3091800 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-09-01 |
| PublicationDateYYYYMMDD | 2021-09-01 |
| PublicationDate_xml | – month: 09 year: 2021 text: 2021-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE communications letters |
| PublicationTitleAbbrev | LCOMM |
| PublicationYear | 2021 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref8 bengio (ref6) 2013 ref9 ref4 ref3 ref5 marsh (ref7) 2013 ref2 ref1 |
| References_xml | – start-page: 899 year: 2013 ident: ref6 article-title: Generalized denoising auto-encoders as generative models publication-title: Proc Adv Neural Inf Process Syst – ident: ref4 doi: 10.1109/COMST.2019.2924243 – ident: ref5 doi: 10.1145/1390156.1390294 – ident: ref8 doi: 10.1109/TBC.2002.804034 – ident: ref2 doi: 10.1109/COMST.2019.2926625 – ident: ref1 doi: 10.1109/MCOM.001.1900664 – ident: ref3 doi: 10.1109/MCOM.2019.1800610 – year: 2013 ident: ref7 publication-title: Introduction to continuous entropy – ident: ref9 doi: 10.1109/MSP.2015.2398954 |
| SSID | ssj0008251 |
| Score | 2.5335813 |
| Snippet | This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE... |
| SourceID | swepub proquest crossref ieee |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2983 |
| SubjectTerms | Decoding Demodulation Encoding Internet of Things Learning Machine learning noise learning based denoising autoencoder Noise measurement Noise reduction precise localization Random variables signal restoration symbol demodulation Training |
| Title | Noise Learning-Based Denoising Autoencoder |
| URI | https://ieeexplore.ieee.org/document/9462839 https://www.proquest.com/docview/2571221671 https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302636 |
| Volume | 25 |
| WOSCitedRecordID | wos000694697800046&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE customDbUrl: eissn: 1558-2558 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0008251 issn: 1089-7798 databaseCode: RIE dateStart: 19970101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLa2iQMceA3EYKAeEAeg25K0SXMcGxMHNjgA2i1q0xQmUIu2jt9PknbVkBASl75kR5Gdxk5ifwY49w1mmSLYJZhT12NSmkPCnquJSah8FhEc22ITbDIJplP-WIPrKhdGKWWDz1THPNqz_DiTS7NV1uUmkZLwOtQZY0WuVjXrmhTMIpiea4-RB6sEmR7v3g8exmO9FMSoQ7R5DEw225oRslVVfjqY66Ch1tCMdv7XxV3YLh1Kp1-MgD2oqXQfttZgBptwOclmC-WUUKqv7o22XLEzVKn-rN-d_jLPDJ5lrOYH8Dy6fRrcuWWNBFcSxnKXhyhBNJIK6zvBAUJSeiGOFU30lYRM4gTJJPI9LJmKtAJozLhkkWYPcYTJITTSLFVH4CQM0ygyB4OUe4EySynJkiRG3PMxo7wFaCU0IUsAcVPH4kPYhUSPCytoYQQtSkG34Kri-SzgM_6kbhqJVpSlMFvQXulGlH_YQuipBmGMKEMtuCj0VfEZyOzh7KUvtGrEe_6m28eU0OPfmz-BTdOJInCsDY18vlSnsCG_8tlifmZH2Tcghs1n |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLZ4ScCBN2I8e0AcgI7FaZPmOAbTENvgMBC3qE1TmEAb2jp-P0nbVUNCSFz6kh1Fdho7if0Z4NS3mGWaoktRMNfjStlDwppriGmofR5RjLNiE7zbDV5exOMcXJa5MFrrLPhMV-1jdpYfD9XEbpVdCZtIScU8LPqehyTP1irnXZuEmYfTC-MzimCaIlMTV-3GQ6djFoNIqtQYyMDms82Yoayuyk8XcxY2NDM1zfX_dXID1gqX0qnnY2AT5vRgC1ZngAa34bw77I-1U4CpvrrXxnbFzo0emM_m3alP0qFFtIz1aAeemre9RsstqiS4inKeuiIkCWGR0mjuFANClPJCjDVLzJWGXGFCVBL5HiquI6MCFnOheGTYQ4yQ7sLCYDjQe-AkHFkU2aNBJrxA28WU4kkSE-H5yJmoAJkKTaoCQtxWsviQ2VKiJmQmaGkFLQtBV-Ci5PnMATT-pN62Ei0pC2FW4HCqG1n8Y2NpJhuCSBgnFTjL9VXyWdDsm_5zXRrVyPf0zbSPjLL935s_geVWr9OW7bvu_QGs2A7lYWSHsJCOJvoIltRX2h-PjrMR9w36atCu |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Noise+Learning-Based+Denoising+Autoencoder&rft.jtitle=IEEE+communications+letters&rft.au=Lee%2C+Woong-Hee&rft.au=Ozger%2C+Mustafa&rft.au=Challita%2C+Ursula&rft.au=Sung%2C+Ki+Won&rft.date=2021-09-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1089-7798&rft.eissn=1558-2558&rft.volume=25&rft.issue=9&rft.spage=2983&rft_id=info:doi/10.1109%2FLCOMM.2021.3091800&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1089-7798&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1089-7798&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1089-7798&client=summon |