RETRACTED: DAE‐GAN: An autoencoder based adversarial network for Gaussian denoising
Image denoising is one of the most classic problems in computer vision for restoring corrupted images. It has been approached by using various traditional state of the art architectures in convolutional neural network (CNN), which has demonstrated considerably better results than the prior methods....
Gespeichert in:
| Veröffentlicht in: | Expert systems Jg. 42; H. 2 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Oxford
Blackwell Publishing Ltd
01.02.2025
|
| Schlagworte: | |
| ISSN: | 0266-4720, 1468-0394 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Image denoising is one of the most classic problems in computer vision for restoring corrupted images. It has been approached by using various traditional state of the art architectures in convolutional neural network (CNN), which has demonstrated considerably better results than the prior methods. There has been recent advancements in approaching the problem using generative adversarial networks (GAN), which has shown considerable promise. In this paper, we propose a novel denoising adversarial architecture to generate denoised image samples from a noisy distribution. A denoising autoencoder has been employed as the Generator to learn image distributions and generate denoised images while the discriminator penalizes the generated output. We employ an additive loss comprising of root mean square and mean absolute error for the Generator function. The model is trained adversarially followed by extensive experiments. We achieved PSNR and SSIM values comparable to the state‐of‐the‐art for a range of blind and non‐blind Gaussian noise. |
|---|---|
| AbstractList | Image denoising is one of the most classic problems in computer vision for restoring corrupted images. It has been approached by using various traditional state of the art architectures in convolutional neural network (CNN), which has demonstrated considerably better results than the prior methods. There has been recent advancements in approaching the problem using generative adversarial networks (GAN), which has shown considerable promise. In this paper, we propose a novel denoising adversarial architecture to generate denoised image samples from a noisy distribution. A denoising autoencoder has been employed as the Generator to learn image distributions and generate denoised images while the discriminator penalizes the generated output. We employ an additive loss comprising of root mean square and mean absolute error for the Generator function. The model is trained adversarially followed by extensive experiments. We achieved PSNR and SSIM values comparable to the state‐of‐the‐art for a range of blind and non‐blind Gaussian noise. |
| Author | Satapathy, Suresh Chandra Lin, Hong Samanta, Abhishek Saha, Aheli |
| Author_xml | – sequence: 1 givenname: Abhishek orcidid: 0000-0001-7502-7070 surname: Samanta fullname: Samanta, Abhishek email: abhisheksamanta60@gmail.com organization: Kalinga Institute of Industrial Technology (Deemed to be University) – sequence: 2 givenname: Aheli surname: Saha fullname: Saha, Aheli email: ahelis340@gmail.com organization: Kalinga Institute of Industrial Technology (Deemed to be University) – sequence: 3 givenname: Suresh Chandra orcidid: 0000-0001-8236-4104 surname: Satapathy fullname: Satapathy, Suresh Chandra organization: Kalinga Institute of Industrial Technology (Deemed to be University) – sequence: 4 givenname: Hong surname: Lin fullname: Lin, Hong email: linh@uhd.edu organization: University of Houston Downtown |
| BookMark | eNp9kEFOwzAURC1UJEphwwkssUNKsRPHSbqL2lCQKpBKkGBluckPcgl2sVNKdxyBM3ISUsIKIf5mNm_mj-YQ9bTRgNAJJUPa3jm8ue2Q-hFJ9lCfMh57JEhYD_WJz7nHIp8coEPnloQQGkW8j-7mWT5Px3k2GeFJmn2-f0zT6xFONZbrxoAuTAkWL6SDEsvyFayTVskaa2g2xj7hylg8lWvnlNS4BG2UU_rxCO1XsnZw_KMDlF9k-fjSm91Mr8bpzCvabokXBhHjLAkJkUFYSR6HRFYsLoMAGI3axrKKFouoIkUShqXPfEYoB0howWKAKhig0y52Zc3LGlwjlmZtdftRBDTkfkwCxlrqrKMKa5yzUImVVc_SbgUlYrea2K0mvldrYfILLlQjG2V0Y6Wq_7bQzrJRNWz_CRfZ_e1D5_kCvvGAyQ |
| CitedBy_id | crossref_primary_10_2478_amns_2023_2_01565 crossref_primary_10_1111_exsy_13504 crossref_primary_10_1088_1361_6501_ad99f2 crossref_primary_10_1109_TGRS_2022_3217402 |
| Cites_doi | 10.1109/TIP.2012.2235847 10.1109/TIP.2017.2662206 10.1177/1550147720923529 10.1201/9780367806941 10.1007/978-3-642-34481-7_42 10.1109/CVPR.2012.6247952 10.1109/TIP.2007.901238 10.1109/CVPR.2014.366 10.1109/CVPR.2018.00333 10.1109/CVPR.2018.00338 10.1109/CVPR.2017.19 |
| ContentType | Journal Article |
| Copyright | 2021 John Wiley & Sons Ltd. 2025 John Wiley & Sons, Ltd. |
| Copyright_xml | – notice: 2021 John Wiley & Sons Ltd. – notice: 2025 John Wiley & Sons, Ltd. |
| DBID | AAYXX CITATION 7SC 7TB 8FD F28 FR3 JQ2 L7M L~C L~D |
| DOI | 10.1111/exsy.12709 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1468-0394 |
| EndPage | n/a |
| ExternalDocumentID | 10_1111_exsy_12709 EXSY12709 |
| Genre | article Correction/Retraction |
| GroupedDBID | -~X .3N .4S .DC .GA .Y3 05W 0B8 0R~ 10A 1OB 1OC 29G 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6TJ 702 77K 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8VB 930 9M8 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABDPE ABEML ABLJU ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACFBH ACGFS ACIWK ACNCT ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMHC ADNMO ADOZA ADXAS ADZMN ADZOD AEEZP AEIGN AEIMD AEMOZ AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AHEFC AHQJS AI. AITYG AIURR AIWBW AJBDE AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CAG COF CS3 CWDTD D-E D-F DC6 DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBA EBR EBS EBU EDO EJD EMK EST ESX F00 F01 F04 FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K1G K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MK~ MRFUL MRSTM MSFUL MSSTM MVM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QWB R.K RIG RIWAO RJQFR ROL RX1 SAMSI SUPJJ TAE TH9 TN5 TUS UB1 VH1 W8V W99 WBKPD WH7 WIH WIK WLBEL WOHZO WQJ WRC WXSBR WYISQ XG1 ZL0 ZZTAW ~02 ~IA ~WT 77I AAMMB AAYXX ADMLS AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AIDQK AIDYY AIQQE CITATION O8X 7SC 7TB 8FD F28 FR3 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c1469-5374649500a35fa6850af48d33e417266af7bb7f0c955d2424016ee91c48eef3 |
| IEDL.DBID | DRFUL |
| ISSN | 0266-4720 |
| IngestDate | Sat Nov 29 14:55:54 EST 2025 Sat Nov 29 07:20:10 EST 2025 Tue Nov 18 22:01:32 EST 2025 Fri Jan 17 09:36:49 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c1469-5374649500a35fa6850af48d33e417266af7bb7f0c955d2424016ee91c48eef3 |
| Notes | ObjectType-Correction/Retraction-1 SourceType-Scholarly Journals-1 content type line 14 |
| ORCID | 0000-0001-7502-7070 0000-0001-8236-4104 |
| PQID | 3156280344 |
| PQPubID | 32130 |
| PageCount | 7 |
| ParticipantIDs | proquest_journals_3156280344 crossref_primary_10_1111_exsy_12709 crossref_citationtrail_10_1111_exsy_12709 wiley_primary_10_1111_exsy_12709_EXSY12709 |
| PublicationCentury | 2000 |
| PublicationDate | February 2025 2025-02-00 20250201 |
| PublicationDateYYYYMMDD | 2025-02-01 |
| PublicationDate_xml | – month: 02 year: 2025 text: February 2025 |
| PublicationDecade | 2020 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Expert systems |
| PublicationYear | 2025 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | 2010; 11 2012 2017; 26 2020 2019; 79 2020; 16 2018 2017 2008; 21 2016 2004 2015 2014 2012; 22 2007; 16 Larsen A. B. L. (e_1_2_9_11_1) 2016 e_1_2_9_10_1 e_1_2_9_13_1 Zhong Y. (e_1_2_9_20_1) 2019; 79 e_1_2_9_12_1 Motwani M. C. (e_1_2_9_14_1) 2004 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_5_1 Vincent P. (e_1_2_9_16_1) 2010; 11 Yan Q. (e_1_2_9_18_1) 2017 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 Hong Z. (e_1_2_9_8_1) 2020 e_1_2_9_15_1 e_1_2_9_17_1 Jain V. (e_1_2_9_9_1) 2008; 21 e_1_2_9_19_1 |
| References_xml | – volume: 16 start-page: 2080 issue: 8 year: 2007 end-page: 2095 article-title: Image denoising by sparse 3‐D transform‐domain collaborative filtering publication-title: IEEE Transactions on Image Processing – volume: 21 start-page: 769 year: 2008 end-page: 776 article-title: Natural image denoising with convolutional networks publication-title: Advances in Neural Information Processing Systems – volume: 11 start-page: 3371 issue: 12 year: 2010 end-page: 3408 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: Journal of Machine Learning Research – start-page: 341 year: 2012 end-page: 349 – start-page: 2862 year: 2014 end-page: 2869 – start-page: 4140 year: 2020 end-page: 4149 – volume: 79 start-page: 1 year: 2019 end-page: 13 article-title: A generative adversarial network for image denoising publication-title: Multimedia Tools and Applications – start-page: 1558 year: 2016 end-page: 1566 – year: 2020 – volume: 16 issue: 5 year: 2020 article-title: DNAE‐GAN: Noise‐free acoustic signal generator by integrating autoencoder and generative adversarial network publication-title: International Journal of Distributed Sensor Networks, – start-page: 3204 year: 2018 end-page: 3213 – volume: 26 start-page: 3142 issue: 7 year: 2017 end-page: 3155 article-title: Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising publication-title: IEEE Transactions on Image Processing – start-page: 487 year: 2017 end-page: 495 – start-page: 3155 year: 2018 end-page: 3164 – start-page: 4681 year: 2017 end-page: 4690 – start-page: 27 year: 2004 end-page: 30 – start-page: 2392 year: 2012 end-page: 2399 – volume: 22 start-page: 1620 issue: 4 year: 2012 end-page: 1630 article-title: Nonlocally centralized sparse representation for image restoration publication-title: IEEE Transactions on Image Processing – year: 2015 – volume: 21 start-page: 769 year: 2008 ident: e_1_2_9_9_1 article-title: Natural image denoising with convolutional networks publication-title: Advances in Neural Information Processing Systems – ident: e_1_2_9_6_1 doi: 10.1109/TIP.2012.2235847 – start-page: 1558 volume-title: International Conference on Machine Learning year: 2016 ident: e_1_2_9_11_1 – ident: e_1_2_9_19_1 doi: 10.1109/TIP.2017.2662206 – ident: e_1_2_9_10_1 doi: 10.1177/1550147720923529 – volume: 79 start-page: 1 year: 2019 ident: e_1_2_9_20_1 article-title: A generative adversarial network for image denoising publication-title: Multimedia Tools and Applications – ident: e_1_2_9_2_1 doi: 10.1201/9780367806941 – ident: e_1_2_9_17_1 doi: 10.1007/978-3-642-34481-7_42 – ident: e_1_2_9_3_1 doi: 10.1109/CVPR.2012.6247952 – ident: e_1_2_9_5_1 doi: 10.1109/TIP.2007.901238 – ident: e_1_2_9_7_1 doi: 10.1109/CVPR.2014.366 – start-page: 27 volume-title: Proceedings of GSPX year: 2004 ident: e_1_2_9_14_1 – ident: e_1_2_9_4_1 doi: 10.1109/CVPR.2018.00333 – ident: e_1_2_9_15_1 – ident: e_1_2_9_13_1 doi: 10.1109/CVPR.2018.00338 – volume: 11 start-page: 3371 issue: 12 year: 2010 ident: e_1_2_9_16_1 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: Journal of Machine Learning Research – start-page: 4140 volume-title: Proceedings of the AAAI Conference on Artificial Intelligence year: 2020 ident: e_1_2_9_8_1 – ident: e_1_2_9_12_1 doi: 10.1109/CVPR.2017.19 – start-page: 487 volume-title: Advances in neural information processing systems year: 2017 ident: e_1_2_9_18_1 |
| SSID | ssj0001776 |
| Score | 2.3455129 |
| SecondaryResourceType | retracted_publication |
| Snippet | Image denoising is one of the most classic problems in computer vision for restoring corrupted images. It has been approached by using various traditional... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| SubjectTerms | computer vision convolutional neural networks deep learning generating adversarial networks |
| Title | RETRACTED: DAE‐GAN: An autoencoder based adversarial network for Gaussian denoising |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fexsy.12709 https://www.proquest.com/docview/3156280344 |
| Volume | 42 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library customDbUrl: eissn: 1468-0394 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001776 issn: 0266-4720 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFD7MzQdfnFecTgnoi0KlXZI2Hb6U3XwYQ-Ym86mkbQID6WTdRN_8Cf5Gf4lJ124TRBCfWsppKcm5fCfJOR_AhXrqRjUmDIuIwCAUmwbDVmDgiEksVE4UphVyD12n12OjkXtXgJu8FmbRH2K54KYtI_XX2sB5kKwZuXhN3q71vqm7ASV1waQIpWa_PewuPbHlpORyKs2wDeLUzKw9qT7Js3r7e0Baocx1rJoGm3b5f7-5A9sZyETeQit2oSDiPSjnBA4os-d9GPZbg77XUM6rjppe6_P9o-P16siLEZ_PJrrFZaTEdaCLENfMzQnX-orixdlxpAAv6vB5ogsxkXJgk7FeeTiAQbs1aNwaGc-CESo_6RoUO8RWiZJpckwltxk1uSQswlgQhW9sm0snCBxphi6lka4nUThRCNcKCRNC4kMoxpNYHAFinDCTmoIKmxEieKCyUzfgChNKKkNLVuAyH2s_zHqQayqMJz_PRfRw-elwVeB8Kfu86Lzxo1Q1nzI_s77Exyop1axbhFTgKp2cX77gt0b3j-nd8V-ET2CrpqmA0wPcVSjOpnNxCpvhy2ycTM8yTfwCKnbhvg |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB60CnqxPrE-F_SiEEm6u8mmt6CtirVIrVJPYZPsQkFSMa3ozZ_gb_SXuJOmVUEE8RbCJITdeXyzmZkPYN_c9ZOqUJbDVGQxTm1LUCeyaCI0VSYnivMOudum12qJbte_KmpzsBdmNB9icuCGlpH7azRwPJD-YuXqOXs5wh-n_jTMMKNHvAQzJ-3GTXPiih0vZ5czeYZrMa9qF_NJsZTn8-nvEekTZn4Fq3m0aZT_-Z2LsFDATBKM9GIJplS6DOUxhQMpLHoFbtr1Tjs4Nu6rRk6C-vvr22nQqpEgJXI46OOQy8SIY6hLiETu5kyixpJ0VD1ODOQlp3KYYSsmMS6s38Ozh1XoNOqd4zOrYFqwYuMpfYtTj7kmVbJtSbmWruC21EwklCpmEI7rSu1Fkaft2Oc8wY4SgxSV8p2YCaU0XYNS2k_VOhAhmbC5rbhyBWNKRiY_9SNpUKHmOnZ0BQ7Gix3GxRRyJMO4D8fZCC5XmC9XBfYmsg-j2Rs_Sm2N9yws7C8LqUlLkXeLsQoc5rvzyxvCevf6Lr_a-IvwLsyddS6bYfO8dbEJ81UkBs7LubegNHgcqm2YjZ8Gvexxp1DLD7P-5a4 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB7UinixPrE-F_SiEEm6u8nGW-hLsZRSW6mnsEl2oSBpMa3ozZ_gb_SXuJsmbQURxFsIkxB25_HNZmY-gHN1143KTBgWEYFBKDYNhq3AwBGTWKicKEw75B6aTqvF-n23ndXm6F6Y6XyI2YGbtozUX2sDF6NILli5eE3ervSPU3cZCoS6trLLQrVT7zVnrthyUnY5lWfYBnHKZjafVJfyzJ_-HpHmMHMRrKbRpl7853duwkYGM5E31YstWBLxNhRzCgeUWfQO9Dq1bserKPd1jape7fP9o-G1rpEXIz4ZD_WQy0iJ61AXIa65mxOuNRbF0-pxpCAvavBJolsxkXJhw4E-e9iFbr3WrdwYGdOCESpP6RoUO8RWqZJpckwltxk1uSQswlgQhXBsm0snCBxphi6lke4oUUhRCNcKCRNC4j1YiYex2AfEOGEmNQUVNiNE8EDlp27AFSqUVIaWLMFFvth-mE0h12QYT36ejejl8tPlKsHZTHY0nb3xo9RRvmd-Zn-Jj1Vaqnm3CCnBZbo7v7zBr_XvH9Org78In8Jau1r3m7etu0NYL2te4LSa-whWxs8TcQyr4ct4kDyfZFr5BWXq5Sk |
| 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=RETRACTED%3A+DAE%E2%80%90GAN%3A+An+autoencoder+based+adversarial+network+for+Gaussian+denoising&rft.jtitle=Expert+systems&rft.au=Samanta%2C+Abhishek&rft.au=Saha%2C+Aheli&rft.au=Satapathy%2C+Suresh+Chandra&rft.au=Lin%2C+Hong&rft.date=2025-02-01&rft.issn=0266-4720&rft.eissn=1468-0394&rft.volume=42&rft.issue=2&rft.epage=n%2Fa&rft_id=info:doi/10.1111%2Fexsy.12709&rft.externalDBID=10.1111%252Fexsy.12709&rft.externalDocID=EXSY12709 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0266-4720&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0266-4720&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0266-4720&client=summon |