Enhanced CNN for image denoising
Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation....
Gespeichert in:
| Veröffentlicht in: | CAAI Transactions on Intelligence Technology Jg. 4; H. 1; S. 17 - 23 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Beijing
The Institution of Engineering and Technology
01.03.2019
John Wiley & Sons, Inc Wiley |
| Schlagworte: | |
| ISSN: | 2468-2322, 2468-6557, 2468-2322 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising. |
|---|---|
| AbstractList | Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising. |
| Author | Wang, Junqian Luo, Nan Xu, Yong Fei, Lunke Wen, Jie Tian, Chunwei |
| Author_xml | – sequence: 1 givenname: Chunwei surname: Tian fullname: Tian, Chunwei organization: 2Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, People's Republic of China – sequence: 2 givenname: Yong surname: Xu fullname: Xu, Yong email: yongxu@ymail.com organization: 2Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, People's Republic of China – sequence: 3 givenname: Lunke surname: Fei fullname: Fei, Lunke organization: 3School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, People's Republic of China – sequence: 4 givenname: Junqian surname: Wang fullname: Wang, Junqian organization: 2Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, People's Republic of China – sequence: 5 givenname: Jie surname: Wen fullname: Wen, Jie organization: 2Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, People's Republic of China – sequence: 6 givenname: Nan surname: Luo fullname: Luo, Nan organization: 4Institute of Automation Heilongjiang Academy of Sciences, Harbin 150090, People's Republic of China |
| BookMark | eNqFkV1LwzAYhYNMUKe3XhcEL4TNNB9tcqlj6kAmSL0Ob9NkZsxmph2yf29qRYY4vMoH55znzckJGtS-Ngidp3icYiav2-DaMcGpiEfODtAxYZkYEUrIYGd_hM6aZokxTqWUnObHKJnWr1BrUyWT-TyxPiTuDRYmqUztXePqxSk6tLBqzNn3OkQvd9Ni8jB6fLqfTW4eR5pzykelLTlJBfCI0lBWVIssYzwTNtcUBOesMgJMvIdcZIxIoKbUVqcyCgS3dIhmfW7lYanWIY4RtsqDU18XPiwUhNbplVGZiSBiJUBJGZMd1RjMCdUVcCuzmHXRZ62Df9-YplVLvwl1HF9RLFOZS0xlVI17lQ6-aYKxP9QUq65U1ZWqulJVV2o0sF8G7Vpona_bAG6135b3tg-3Mtt_IGoyK8jtXfyiWOsQXfVOZ3aesBdz-Ye4eJ4VO-nrytJPwPmmnQ |
| CitedBy_id | crossref_primary_10_3390_electronics13091676 crossref_primary_10_1007_s11600_024_01339_x crossref_primary_10_3390_electronics11213535 crossref_primary_10_1016_j_inffus_2021_07_001 crossref_primary_10_1016_j_eswa_2023_120628 crossref_primary_10_1016_j_knosys_2021_106949 crossref_primary_10_1007_s11042_021_11521_8 crossref_primary_10_1109_TCYB_2018_2884715 crossref_primary_10_1007_s11554_020_01060_0 crossref_primary_10_1016_j_aap_2022_106836 crossref_primary_10_3390_electronics12092146 crossref_primary_10_1016_j_dsp_2025_105309 crossref_primary_10_1007_s12293_025_00463_5 crossref_primary_10_3390_info15030133 crossref_primary_10_35848_1347_4065_acbda2 crossref_primary_10_1109_TCBB_2022_3205217 crossref_primary_10_1007_s11600_022_00912_6 crossref_primary_10_1016_j_jvcir_2021_103425 crossref_primary_10_1016_j_imavis_2024_104974 crossref_primary_10_1007_s11227_024_06045_5 crossref_primary_10_1088_2057_1976_ad4f91 crossref_primary_10_1007_s11227_025_07742_5 crossref_primary_10_1109_ACCESS_2021_3086811 crossref_primary_10_1049_iet_ipr_2020_0717 crossref_primary_10_1007_s12524_020_01155_y crossref_primary_10_1007_s00138_024_01573_9 crossref_primary_10_1109_TIV_2023_3327501 crossref_primary_10_1049_cit2_12172 crossref_primary_10_1109_ACCESS_2020_2999750 crossref_primary_10_1109_ACCESS_2023_3264604 crossref_primary_10_1109_TCBB_2020_3018137 crossref_primary_10_1007_s00530_025_01859_6 crossref_primary_10_1016_j_asoc_2020_106440 crossref_primary_10_1088_1742_6596_1854_1_012040 crossref_primary_10_1088_1757_899X_1055_1_012116 crossref_primary_10_1109_ACCESS_2021_3106020 crossref_primary_10_1016_j_iswa_2023_200211 crossref_primary_10_1016_j_inffus_2023_102043 crossref_primary_10_1007_s10489_023_04895_9 crossref_primary_10_1016_j_ab_2024_115491 crossref_primary_10_1109_TNNLS_2020_3016321 crossref_primary_10_1109_TC_2025_3558579 crossref_primary_10_1016_j_jvcir_2020_102774 crossref_primary_10_3390_s25092672 crossref_primary_10_1049_ipr2_12915 crossref_primary_10_1007_s11042_023_16209_9 crossref_primary_10_3390_rs14246300 crossref_primary_10_1088_1361_6560_ad7e78 crossref_primary_10_3390_app15105339 crossref_primary_10_1109_ACCESS_2020_2970143 crossref_primary_10_1007_s11042_023_17670_2 crossref_primary_10_1109_ACCESS_2019_2936861 crossref_primary_10_1016_j_knosys_2020_106235 crossref_primary_10_1016_j_neunet_2020_07_025 crossref_primary_10_1007_s11760_023_02944_x crossref_primary_10_1002_ett_3843 crossref_primary_10_3390_electronics12245019 crossref_primary_10_1007_s11042_023_16452_0 crossref_primary_10_1016_j_neunet_2019_12_024 crossref_primary_10_1109_TGRS_2020_3034752 crossref_primary_10_3390_s25082615 crossref_primary_10_1049_joe_2019_1151 crossref_primary_10_1007_s10489_022_04313_6 crossref_primary_10_3390_bios15010019 crossref_primary_10_1016_j_jksuci_2023_02_003 crossref_primary_10_1038_s41598_023_38335_y crossref_primary_10_1364_AO_501143 crossref_primary_10_1007_s10489_022_03717_8 crossref_primary_10_3390_electronics12183770 crossref_primary_10_1016_j_ultrasmedbio_2025_02_013 crossref_primary_10_1016_j_engappai_2025_110275 crossref_primary_10_1007_s11042_023_16346_1 crossref_primary_10_1007_s11053_025_10473_2 crossref_primary_10_1109_TCSVT_2023_3348804 crossref_primary_10_1016_j_procs_2024_04_204 crossref_primary_10_3390_rs14051243 crossref_primary_10_1016_j_neunet_2023_05_025 crossref_primary_10_1109_ACCESS_2021_3084951 crossref_primary_10_1155_2020_6097167 crossref_primary_10_1007_s11063_023_11359_1 crossref_primary_10_1109_TSMC_2025_3552621 crossref_primary_10_1109_ACCESS_2019_2937098 crossref_primary_10_3390_s25020317 crossref_primary_10_1007_s10115_023_01965_9 crossref_primary_10_1109_ACCESS_2024_3411709 crossref_primary_10_3390_app13010028 crossref_primary_10_1016_j_patrec_2023_05_015 crossref_primary_10_1007_s11042_022_13096_4 crossref_primary_10_3390_s23187713 crossref_primary_10_1109_ACCESS_2021_3092425 crossref_primary_10_1049_cit2_12256 crossref_primary_10_1137_20M1340654 crossref_primary_10_1007_s11760_024_03093_5 crossref_primary_10_1016_j_inffus_2025_103013 crossref_primary_10_1016_j_eswa_2021_114900 crossref_primary_10_1049_cit2_12019 crossref_primary_10_1109_TBME_2024_3501732 crossref_primary_10_1109_ACCESS_2019_2939167 crossref_primary_10_1007_s11042_019_08089_9 crossref_primary_10_1109_ACCESS_2022_3162608 crossref_primary_10_1007_s00371_022_02749_y crossref_primary_10_3390_s24144578 crossref_primary_10_1109_TCSVT_2020_3009235 crossref_primary_10_1002_gamm_202470004 crossref_primary_10_3390_electronics11203375 crossref_primary_10_1007_s11760_025_04776_3 |
| Cites_doi | 10.1109/ICCV.2015.123 10.1007/s11263‐008‐0197‐6 10.1109/ICCV.2011.6126278 10.1007/s00791‐004‐0150‐3 10.1109/CVPR.2014.349 10.1137/050644999 10.1109/TCYB.2016.2536638 10.1109/TIP.2018.2839891 10.1007/978-3-030-01264-9_19 10.1109/CVPR.2014.366 10.1109/MSP.2017.2717489 10.1109/TIP.2009.2028250 10.1109/CVPR.2017.623 10.1109/CVPRW.2018.00121 10.1109/CVPR.2018.00338 10.1109/CVPR.2016.90 10.1145/3072959.3073708 10.1109/TMI.2018.2823756 10.1016/j.trit.2017.03.001 10.1109/TIP.2014.2316423 10.1109/TIP.2017.2662206 10.1109/CVPR.2012.6247952 10.1109/83.563320 10.1109/ICCV.2009.5459452 10.1109/TIP.2007.901238 10.1109/TIP.2012.2235847 10.1109/ICTAI.2017.00192 10.1109/ICDMW.2016.0041 10.1109/TBME.2012.2217493 10.1007/s13369‐017‐2696‐7 10.1109/CVPR.2017.300 |
| ContentType | Journal Article |
| Copyright | 2019 CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of Chongqing University of Technology 2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2019 CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of Chongqing University of Technology – notice: 2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | IDLOA 24P AAYXX CITATION 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA |
| DOI | 10.1049/trit.2018.1054 |
| DatabaseName | IET Digital Library Open Access Wiley Online Library Open Access CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest - Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2468-2322 |
| EndPage | 23 |
| ExternalDocumentID | oai_doaj_org_article_6e18a2f9aab344918a5ee0523cda5f96 10_1049_trit_2018_1054 CIT2BF00055 |
| Genre | article |
| GrantInformation_xml | – fundername: Shenzhen Municipal Science and Technology Innovation Council grantid: JCYJ20170811155725434 – fundername: Guangdong Province high-level personnel of special support program grantid: 2016TX03X164 – fundername: Shenzhen Municipal Science and Technology Innovation Council funderid: JCYJ20170811155725434 – fundername: Guangdong Province high‐level personnel of special support program funderid: 2016TX03X164 |
| GroupedDBID | 0R 0SF 24P 6I. AACTN AAFTH AAJGR ABMAC ACGFS ADBBV AEXQZ AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV BFFAM EBS EJD FDB GROUPED_DOAJ IDLOA O9- OCL OK1 RIE RIG RUI SSZ 0R~ 1OC AAEDW AAHHS AAHJG AALRI AAXUO ABQXS ACCFJ ACCMX ACESK ACXQS ADVLN ADZOD AEEZP AEQDE AFKRA AIWBW AJBDE AKRWK ALUQN ARAPS ARCSS AVUZU BENPR BGLVJ CCPQU HCIFZ IAO ITC K7- M41 M43 NCXOZ PIMPY ROL AAMMB AAYWO AAYXX ACVFH ADCNI ADMLS AEFGJ AEUPX AFFHD AFPUW AGXDD AIDQK AIDYY AIGII AKBMS AKYEP CITATION ICD PHGZM PHGZT PQGLB WIN 8FE 8FG ABUWG AZQEC DWQXO GNUQQ JQ2 P62 PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c5535-bfb5218a5468cabd3c8664568f7c3a8554de8aed3ca786429a3ebcfc19f7c85f3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 128 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000597144900003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2468-2322 2468-6557 |
| IngestDate | Fri Oct 03 12:50:55 EDT 2025 Sat Jul 26 00:20:46 EDT 2025 Tue Nov 18 22:35:12 EST 2025 Wed Oct 29 21:25:59 EDT 2025 Wed Jan 22 16:31:40 EST 2025 Tue Jan 05 21:44:20 EST 2021 Thu May 09 18:00:46 EDT 2019 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | dilated convolutions residual learning enhanced CNN convolutional neural denoising network batch normalisation techniques deep network architecture image denoising flexible architectures image restoration Deeper networks image representation training difficulties convolution performance saturation deep convolutional neural networks learning (artificial intelligence) image restoration CNN neural nets authors |
| Language | English |
| License | Attribution |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c5535-bfb5218a5468cabd3c8664568f7c3a8554de8aed3ca786429a3ebcfc19f7c85f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/docview/3091979039?pq-origsite=%requestingapplication% |
| PQID | 3091979039 |
| PQPubID | 6852857 |
| PageCount | 7 |
| ParticipantIDs | wiley_primary_10_1049_trit_2018_1054_CIT2BF00055 doaj_primary_oai_doaj_org_article_6e18a2f9aab344918a5ee0523cda5f96 proquest_journals_3091979039 crossref_primary_10_1049_trit_2018_1054 iet_journals_10_1049_trit_2018_1054 crossref_citationtrail_10_1049_trit_2018_1054 |
| ProviderPackageCode | IDLOA RUI |
| PublicationCentury | 2000 |
| PublicationDate | March 2019 |
| PublicationDateYYYYMMDD | 2019-03-01 |
| PublicationDate_xml | – month: 03 year: 2019 text: March 2019 |
| PublicationDecade | 2010 |
| PublicationPlace | Beijing |
| PublicationPlace_xml | – name: Beijing |
| PublicationTitle | CAAI Transactions on Intelligence Technology |
| PublicationYear | 2019 |
| Publisher | The Institution of Engineering and Technology John Wiley & Sons, Inc Wiley |
| Publisher_xml | – name: The Institution of Engineering and Technology – name: John Wiley & Sons, Inc – name: Wiley |
| References | Beck, A.; Teboulle, M. (C9) 2009; 18 Frohn, C.; Henn, S.; Witsch, K. (C12) 2004; 7 Kang, E.; Chang, W.; Yoo, J. (C24) 2018; 37 Dabov, K.; Foi, A.; Katkovnik, V. (C6) 2007; 16 Bako, S.; Vogels, T.; McWilliams, B. (C20) 2017; 36 Li, S.; Yin, H.; Fang, L. (C1) 2006; 59 Chan, T.F.; Chen, K (C11) 2006; 5 Roth, S.; Black, M.J. (C43) 2009; 82 Chen, Y.; Pock, T. (C42) 2016; PP Zhang, K.; Zuo, W.; Chen, Y. (C3) 2017; 26 Guo, K.; Wu, S.; Xu, Y. (C36) 2017; 2 Zhang, L.; Zuo, W. (C2) 2017; 34 Dong, W.; Zhang, L.; Shi, G. (C8) 2013; 22 Zhang, K.; Zuo, W.; Zhang, L. (C15) 2018 Duchi, J.; Hazan, E.; Singer, Y. (C31) 2011; 12 Du, B.; Xiong, W.; Wu, J. (C18) 2017; 47 Kinga, D.; Adam, J.B. (C39) 2015; 5 Tian, C.; Zhang, Q.; Sun, G. (C34) 2018; 42 Malfait, M.; Roose, D. (C4) 1996; 6 Fei, L.; Lu, G.; Jia, W. (C35) 2018 Zuo, W.; Zhang, L.; Song, C. (C7) 2014; 23 Krizhevsky, A.; Sutskever, I.; Hinton, G.E. (C33) 2012 2017; 2 2015; 5 2017; 26 2013; 22 2012 2017; 47 2011 2009; 82 2004; 7 2006; 59 2009 2008 2006; 5 2011; 12 2018; 42 2014; 23 2007; 16 2017; 36 2017; 34 2018 2017 2016 2015 2014 2018; 37 1996; 6 2009; 18 2016; PP e_1_2_8_27_2 e_1_2_8_28_2 e_1_2_8_29_2 e_1_2_8_23_2 e_1_2_8_46_2 e_1_2_8_24_2 e_1_2_8_45_2 e_1_2_8_25_2 e_1_2_8_26_2 e_1_2_8_9_2 e_1_2_8_2_2 Kinga D. (e_1_2_8_40_2) 2015; 5 e_1_2_8_4_2 e_1_2_8_3_2 e_1_2_8_6_2 e_1_2_8_5_2 Chen Y. (e_1_2_8_43_2) 2016 e_1_2_8_8_2 e_1_2_8_7_2 Duchi J. (e_1_2_8_32_2) 2011; 12 e_1_2_8_42_2 e_1_2_8_20_2 e_1_2_8_41_2 e_1_2_8_21_2 e_1_2_8_44_2 e_1_2_8_22_2 Krizhevsky A. (e_1_2_8_34_2) 2012 e_1_2_8_16_2 e_1_2_8_39_2 e_1_2_8_17_2 e_1_2_8_38_2 e_1_2_8_18_2 e_1_2_8_19_2 e_1_2_8_12_2 e_1_2_8_35_2 e_1_2_8_13_2 e_1_2_8_14_2 e_1_2_8_37_2 e_1_2_8_15_2 Fei L. (e_1_2_8_36_2) 2018 e_1_2_8_31_2 e_1_2_8_30_2 e_1_2_8_10_2 e_1_2_8_33_2 e_1_2_8_11_2 |
| References_xml | – volume: 36 start-page: 1 issue: 4 year: 2017 end-page: 14 ident: C20 article-title: Kernel-predicting convolutional networks for denoising Monte Carlo renderings publication-title: ACM Trans. Graph – year: 2018 ident: C35 article-title: Feature extraction methods for palmprint recognition: a survey and evaluation publication-title: IEEE Trans. Syst., Man, Cybern. Syst. – volume: 26 start-page: 3142 issue: 7 year: 2017 end-page: 3155 ident: C3 article-title: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising publication-title: IEEE Trans. Image Process. – volume: 37 start-page: 1358 issue: 6 year: 2018 end-page: 1369 ident: C24 article-title: Deep convolutional framelet denoising for low-dose ct via wavelet residual network publication-title: IEEE Trans. Med. Imaging – volume: PP start-page: 1 issue: 99 year: 2016 end-page: 1 ident: C42 article-title: Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 18 start-page: 2419 issue: 11 year: 2009 end-page: 2434 ident: C9 article-title: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems publication-title: IEEE Trans. Image Process. – volume: 16 start-page: 2080 issue: 8 year: 2007 end-page: 2095 ident: C6 article-title: Image denoising by sparse 3-D transform-domain collaborative filtering publication-title: IEEE Trans. Image Process. – volume: 23 start-page: 2459 issue: 6 year: 2014 end-page: 2472 ident: C7 article-title: Gradient histogram estimation and preservation for texture enhanced image denoising publication-title: IEEE Trans. Image Process. – volume: 5 start-page: 615 issue: 2 year: 2006 end-page: 645 ident: C11 article-title: An optimization-based multilevel algorithm for total variation image denoising publication-title: Multiscale. Model. Simul. – volume: 47 start-page: 1017 issue: 4 year: 2017 end-page: 1027 ident: C18 article-title: Stacked convolutional denoising auto-encoders for feature representation publication-title: IEEE Trans Cybern. – volume: 22 start-page: 1620 issue: 4 year: 2013 end-page: 1630 ident: C8 article-title: Nonlocally centralized sparse representation for image restoration publication-title: IEEE Trans. Image Process. – volume: 6 start-page: 549 issue: 4 year: 1996 end-page: 565 ident: C4 article-title: Wavelet-based image denoising using a Markov random field a priori model publication-title: IEEE Trans. Image Process. – start-page: 1097 year: 2012 end-page: 1105 ident: C33 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 42 start-page: 741 issue: 2 year: 2018 end-page: 758 ident: C34 article-title: FFT consolidated sparse and collaborative representation for image classification publication-title: Arab. J. Sci. Eng. – volume: 59 start-page: 3450 issue: 12 year: 2006 end-page: 3459 ident: C1 article-title: Group-sparse representation with dictionary learning for medical image denoising and fusion publication-title: IEEE Trans. Biomed. Eng. – volume: 34 start-page: 172 issue: 5 year: 2017 end-page: 179 ident: C2 article-title: Image restoration: from sparse and low-rank priors to deep priors publication-title: IEEE Signal Process. Mag. – start-page: 4608 year: 2018 end-page: 4622 ident: C15 article-title: FFDNet: toward a fast and flexible solution for CNN based image denoising publication-title: IEEE Trans. Image Process. – volume: 2 start-page: 39 issue: 1 year: 2017 end-page: 47 ident: C36 article-title: Face recognition using both visible light image and near-infrared image and a deep network publication-title: CAAI Trans. Intell. Technol. – volume: 12 start-page: 2121 year: 2011 end-page: 2159 ident: C31 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 199 issue: 3–4 year: 2004 end-page: 206 ident: C12 article-title: Nonlinear multigrid methods for total variation image denoising publication-title: Comput. Vis. Sci. – volume: 5 year: 2015 ident: C39 article-title: A method for stochastic optimization publication-title: Int. Conf. Learn. Representations (ICLR) – volume: 82 start-page: 205 issue: 2 year: 2009 end-page: 229 ident: C43 article-title: Fields of experts publication-title: Int. J. Comput. Vis. – volume: 23 start-page: 2459 issue: 6 year: 2014 end-page: 2472 article-title: Gradient histogram estimation and preservation for texture enhanced image denoising publication-title: IEEE Trans. Image Process. – volume: 47 start-page: 1017 issue: 4 year: 2017 end-page: 1027 article-title: Stacked convolutional denoising auto‐encoders for feature representation publication-title: IEEE Trans Cybern. – volume: 37 start-page: 1358 issue: 6 year: 2018 end-page: 1369 article-title: Deep convolutional framelet denoising for low‐dose ct via wavelet residual network publication-title: IEEE Trans. Med. Imaging – start-page: 2774 year: 2014 end-page: 2781 article-title: Shrinkage fields for effective image restoration – year: 2015 article-title: Adam: a method for stochastic optimization – start-page: 3587 year: 2017 end-page: 3596 article-title: Learning deep CNN denoiser prior for image restoration – start-page: 1026 year: 2015 end-page: 1034 article-title: Delving deep into rectifiers: surpassing human‐level performance on imagenet classification – volume: 7 start-page: 199 issue: 3–4 year: 2004 end-page: 206 article-title: Nonlinear multigrid methods for total variation image denoising publication-title: Comput. Vis. Sci. – start-page: 1026 year: 2015 end-page: 1034 article-title: Delving deep into rectifiers:surpassing human‐level performance on imagenet classification – volume: PP start-page: 1 issue: 99 year: 2016 end-page: 1 article-title: Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – year: 2008 article-title: An efficient primal‐dual hybrid gradient algorithm for total variation image restoration – volume: 5 start-page: 615 issue: 2 year: 2006 end-page: 645 article-title: An optimization‐based multilevel algorithm for total variation image denoising publication-title: Multiscale. Model. Simul. – start-page: 479 year: 2011 end-page: 486 article-title: From learning models of natural image patches to whole image restoration – volume: 5 year: 2015 article-title: A method for stochastic optimization publication-title: Int. Conf. Learn. Representations (ICLR) – start-page: 1097 year: 2012 end-page: 1105 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Process. Syst. – year: 2018 – start-page: 770 year: 2016 end-page: 778 article-title: Deep residual learning for image recognition – volume: 6 start-page: 549 issue: 4 year: 1996 end-page: 565 article-title: Wavelet‐based image denoising using a Markov random field a priori model publication-title: IEEE Trans. Image Process. – start-page: 2862 year: 2014 end-page: 2869 article-title: Weighted nuclear norm minimization with application to image denoising – volume: 34 start-page: 172 issue: 5 year: 2017 end-page: 179 article-title: Image restoration: from sparse and low‐rank priors to deep priors publication-title: IEEE Signal Process. Mag. – 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 Trans. Image Process. – volume: 59 start-page: 3450 issue: 12 year: 2006 end-page: 3459 article-title: Group‐sparse representation with dictionary learning for medical image denoising and fusion publication-title: IEEE Trans. Biomed. Eng. – volume: 82 start-page: 205 issue: 2 year: 2009 end-page: 229 article-title: Fields of experts publication-title: Int. J. Comput. Vis. – volume: 12 start-page: 2121 year: 2011 end-page: 2159 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: J. Mach. Learn. Res. – start-page: 2392 year: 2012 end-page: 2399 article-title: Image denoising: Can plain neural networks compete with BM3D? – volume: 36 start-page: 1 issue: 4 year: 2017 end-page: 14 article-title: Kernel‐predicting convolutional networks for denoising Monte Carlo renderings publication-title: ACM Trans. Graph – volume: 18 start-page: 2419 issue: 11 year: 2009 end-page: 2434 article-title: Fast gradient‐based algorithms for constrained total variation image denoising and deblurring problems publication-title: IEEE Trans. Image Process. – volume: 22 start-page: 1620 issue: 4 year: 2013 end-page: 1630 article-title: Nonlocally centralized sparse representation for image restoration publication-title: IEEE Trans. Image Process. – volume: 42 start-page: 741 issue: 2 year: 2018 end-page: 758 article-title: FFT consolidated sparse and collaborative representation for image classification publication-title: Arab. J. Sci. Eng. – year: 2018 article-title: Feature extraction methods for palmprint recognition: a survey and evaluation publication-title: IEEE Trans. Syst., Man, Cybern. Syst. – start-page: 2272 year: 2009 end-page: 2279 article-title: Non‐local sparse models for image restoration – start-page: 4608 year: 2018 end-page: 4622 article-title: FFDNet: toward a fast and flexible solution for CNN based image denoising publication-title: IEEE Trans. Image Process. – 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 Trans. Image Process. – start-page: 241 year: 2016 end-page: 246 article-title: Medical image denoising using convolutional denoising auto encoders – year: 2017 – volume: 2 start-page: 39 issue: 1 year: 2017 end-page: 47 article-title: Face recognition using both visible light image and near‐infrared image and a deep network publication-title: CAAI Trans. Intell. Technol. – start-page: 3204 year: 2018 end-page: 3213 article-title: Universal denoising networks: a novel CNN architecture for image denoising – year: 2015 – start-page: 3587 year: 2017 end-page: 3596 article-title: Non‐local color image denoising with convolutional neural networks – ident: e_1_2_8_41_2 doi: 10.1109/ICCV.2015.123 – ident: e_1_2_8_44_2 doi: 10.1007/s11263‐008‐0197‐6 – ident: e_1_2_8_39_2 doi: 10.1109/ICCV.2011.6126278 – start-page: 1 issue: 99 year: 2016 ident: e_1_2_8_43_2 article-title: Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – ident: e_1_2_8_13_2 doi: 10.1007/s00791‐004‐0150‐3 – ident: e_1_2_8_24_2 – ident: e_1_2_8_45_2 doi: 10.1109/CVPR.2014.349 – ident: e_1_2_8_12_2 doi: 10.1137/050644999 – ident: e_1_2_8_30_2 – volume: 12 start-page: 2121 year: 2011 ident: e_1_2_8_32_2 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: J. Mach. Learn. Res. – ident: e_1_2_8_19_2 doi: 10.1109/TCYB.2016.2536638 – ident: e_1_2_8_16_2 doi: 10.1109/TIP.2018.2839891 – ident: e_1_2_8_29_2 – ident: e_1_2_8_27_2 doi: 10.1007/978-3-030-01264-9_19 – ident: e_1_2_8_14_2 doi: 10.1109/CVPR.2014.366 – ident: e_1_2_8_3_2 doi: 10.1109/MSP.2017.2717489 – ident: e_1_2_8_10_2 doi: 10.1109/TIP.2009.2028250 – start-page: 1097 year: 2012 ident: e_1_2_8_34_2 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Process. Syst. – ident: e_1_2_8_42_2 – year: 2018 ident: e_1_2_8_36_2 article-title: Feature extraction methods for palmprint recognition: a survey and evaluation publication-title: IEEE Trans. Syst., Man, Cybern. Syst. – ident: e_1_2_8_17_2 doi: 10.1109/CVPR.2017.623 – ident: e_1_2_8_11_2 – ident: e_1_2_8_20_2 – ident: e_1_2_8_23_2 doi: 10.1109/CVPRW.2018.00121 – ident: e_1_2_8_31_2 doi: 10.1109/ICCV.2015.123 – ident: e_1_2_8_15_2 doi: 10.1109/CVPR.2018.00338 – ident: e_1_2_8_28_2 doi: 10.1109/CVPR.2016.90 – ident: e_1_2_8_21_2 doi: 10.1145/3072959.3073708 – ident: e_1_2_8_25_2 doi: 10.1109/TMI.2018.2823756 – ident: e_1_2_8_37_2 doi: 10.1016/j.trit.2017.03.001 – ident: e_1_2_8_8_2 doi: 10.1109/TIP.2014.2316423 – ident: e_1_2_8_22_2 – ident: e_1_2_8_33_2 – ident: e_1_2_8_4_2 doi: 10.1109/TIP.2017.2662206 – ident: e_1_2_8_46_2 doi: 10.1109/CVPR.2012.6247952 – ident: e_1_2_8_5_2 doi: 10.1109/83.563320 – ident: e_1_2_8_6_2 doi: 10.1109/ICCV.2009.5459452 – ident: e_1_2_8_7_2 doi: 10.1109/TIP.2007.901238 – ident: e_1_2_8_9_2 doi: 10.1109/TIP.2012.2235847 – ident: e_1_2_8_38_2 doi: 10.1109/ICTAI.2017.00192 – ident: e_1_2_8_26_2 doi: 10.1109/ICDMW.2016.0041 – ident: e_1_2_8_2_2 doi: 10.1109/TBME.2012.2217493 – volume: 5 year: 2015 ident: e_1_2_8_40_2 article-title: A method for stochastic optimization publication-title: Int. Conf. Learn. Representations (ICLR) – ident: e_1_2_8_35_2 doi: 10.1007/s13369‐017‐2696‐7 – ident: e_1_2_8_18_2 doi: 10.1109/CVPR.2017.300 |
| SSID | ssj0001999537 ssib050169717 ssib050729737 ssib052855658 |
| Score | 2.5020628 |
| Snippet | Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the... |
| SourceID | doaj proquest crossref wiley iet |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 17 |
| SubjectTerms | Artificial neural networks authors B6135 Optical, image and video signal processing batch normalisation techniques C5260B Computer vision and image processing techniques C5290 Neural computing techniques convolution convolutional neural denoising network deep convolutional neural networks deep network architecture Deeper networks dilated convolutions enhanced CNN flexible architectures image denoising Image enhancement image representation image restoration image restoration CNN learning (artificial intelligence) neural nets Noise Noise reduction performance saturation Research Article residual learning training difficulties |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEF5EPHgRRcVo1YCCp2Cafc5Ri6IgRaSKt2WzDyxolbb6-53dpNoepBeP2QybzEyS-WZ38g0hp0x675m1ReABCsw3XAFW1QU3AEa4krn01_vTnez31fMz3M-1-oo1YQ09cGO4c-G7ylQBjKkpY4AH3Pu4lmmdwekT2XYpYS6ZSqsriHs4lTOWRgbnkdo-VnKp2NmWLUShRNaPsWXopws4cx6tpnBzvUk2WpyYXzT3t0VW_Gib5Fejl7Rjn_f6_RzhZj58w-9Bjt-O92FM-nfI4_XVoHdTtC0OCss55UUdaoyfqBMTypraUauEQEyjgrTUxBIy55XxOG6kwlQBDPW1DbYLKKB4oLtkdfQ-8nskF0FxV_lQcu5YaYSpBAgnK-WFrH2oM1LMVNa25f-ObSheddqHZqCjiXQ0kY4mysjZj_xHw3zxp-RltOCPVGSsTgPoR936US_zY0ZO0P66fYMmf17qeEFq8HA7-D2rP1zISGfmwV8xiqgIJJQUMlImry5RSfduB9VlrLzkfP8_1Dsg6zg5NNVrHbI6HX_6Q7Jmv6bDyfgoPbzf3oPvew priority: 102 providerName: Directory of Open Access Journals – databaseName: Wiley Online Library Open Access dbid: 24P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9tAEB5R6KEXoKJVTSm1RKWerDreh3ePEIGKVEUIpYjbap9tpDZBIfD7O7N2EnJAVdXrevwYz8zu5_HsNwCfeBtj5N5XSSRd4fdGqLRXrhJWaytDzUPe9X7zrR2N1O2tvnqyi7_jh1gl3Cgy8nxNAW5d14UEQS0akbjqqTRLUata_gJ2BgPWkl83_GqdZUH8IzJxZkNbjBA-NEvmRq6_bF5iY2XKBP643kziYgN7PkWweQm62Pv_h9-H3R5-lqedv7yGrTg9gPJ8-jMXApTD0ahEFFtOfuM0U-KUNJtQLuENfL84Hw-_Vn3nhMoLwUTlksNlWVmBSnvrAvNKSoRKKrWeWapMC1HZiOO2VfgFoi2Lzic_0CigRGJvYXs6m8Z3UMqkRGhiqoUIvLbSNlLL0DYqytbF5Aqolm_N-J5WnLpb_DL59zbXhnQ1pKshXQv4vJK_6wg1npU8IyOspIgIOw_M5j9MH1dGRlSzSdpaxzjXpHOMlOr2waL3yQJO0ISmD8z7Z2_1cUNqfH05Xh81dyEVcLR0grUYQ7ClW10zXUCdzf0XlczwctycUUGnEIf_fsp7eIXjuiuBO4LtxfwhfoCX_nExuZ8fZ8__A5xaAQo priority: 102 providerName: Wiley-Blackwell |
| Title | Enhanced CNN for image denoising |
| URI | http://digital-library.theiet.org/content/journals/10.1049/trit.2018.1054 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Ftrit.2018.1054 https://www.proquest.com/docview/3091979039 https://doaj.org/article/6e18a2f9aab344918a5ee0523cda5f96 |
| Volume | 4 |
| WOSCitedRecordID | wos000597144900003&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: DOA dateStart: 20180101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssib050729737 issn: 2468-2322 databaseCode: M~E dateStart: 20160101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: K7- dateStart: 20170601 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: BENPR dateStart: 20170601 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: PIMPY dateStart: 20170601 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: WIN dateStart: 20170101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: 24P dateStart: 20170101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR3LbhMx0KItBy4UBIiFElYCiZPVZf0-VSRKRQRdraoA5WR5_SiRIAlJ6LHfzthxGnIoHLhYWnv24ZnxzHg8O4PQayq899RaHFhQGPYbDisrO8yMUoa7irr01_vnj6Jp5MWFarPDbZnDKjcyMQlqN7PRR35MQLEpoSqiTuY_cawaFU9XcwmNPXQQM5UBnx_0h017vvWygP3DiNhka6TqOKa4jxFdMla4pTvaKCXtBx0z8asde_NPqzWpndPD__3gB-h-NjjLd2sOeYju-OkjVA6n39LRfzlomhLs1nLyAwRLCUJoNoneg8fo0-lwPHiPc60EbBkjDHehA0UsDaNcWtM5YiXnYBzJICwxMRbNeWk89BshYc-hDPGdDfatAgDJAnmC9qezqX-KSh4kc7UPFWOOVoabmivuRC09F50PXYHwBmfa5kTisZ7Fd50OtKnSEcc64lhHHBfozQ38fJ1C41bIfiTBDVRMfZ06ZotLnVeS5h6mWQdlTEcoVXHO3kfntnUG-I0X6BUQUOeluLz1VS93oMbno_F2VM9dKNDRhq5bsC1RC1QltvjHlPRgNK77MYSTsWd_f-JzdA9uU-sAtyO0v1r88i_QXXu1miwXPbRX07aX-buXXAfQfhAY2rPrIYy0o7P2K1x9GTW_AQ1ABRU |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFAkuBQQI00ItAeJk1fU-vHtAiIZWtZpaETKonJb1PiASJCFJi_hT_EZmHbshh8KpB67r0djr-XZmdnZ2BuA5zZ1z1JjEMy8T3G_YRBpRJ0xLqblNqW1uvX8Y5GUpzs7kcAN-dXdhQlplpxMbRW0nJsTI9wgaNpnLlMjX0-9J6BoVTle7FhpLWJy4nz9wyzZ_VbxF-b7IsqPDqn-ctF0FEsMYYUntazRZQjPKhdG1JUZwjm6E8LkhOmRtWSe0w3GdC_TOpSauNt7sSyQQzBPkewM2KYI97cHmsDgdflxFddDfYiTvqkNSuRdK6ocMMhE66tI169c0CUCbNnKLNf_2Ty-5MXNHd_63H3QXtlqHOn6zXAH3YMON70N8OP7SpDbE_bKM0S-PR99QccaoZCejEB15AO-v5aMeQm88GbtHEHMvmM2cTxmzNNVcZ1xym2fC8bx2vo4g6WSkTFsoPfTr-KqaA3sqVZCpCjJVQaYRvLykny5LhFxJeRBEfkkVSns3A5PZZ9VqCsUdTjPzUuuaUCrDnJ0LwXtjNa4nHsEzBIxqVc38ylftrlFV74pq9VRNrY9gp8PRimwFogjSBob_mJLqF1V2EFJUGXv8d467cOu4Oh2oQVGebMNtZCGXyXw70FvMzt0TuGkuFqP57Gm7qmL4dN04_Q1hZV0p |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fb9MwED7BQIiXAQJE2GCRQOIpIiS2Yz9u3SoqpmhCBfZmOfYZKkE7dYW_nzsnbenDhJB4dS4_LufzfT6fPwO8Fg0iCu-LKKMpaL4RCuN1V0hnjFOhFCHtev983rStvrw0F0M1Ie-F6fkhNgk39ow0XrOD41WI_YRTMEkmk9VzbZbms2rFbbgjCIxzVdeXSbtNsxAAkok5s-I9RoQfqjV1ozBvdx-xE5oSgz8FnBmudsDnnxA2xaDxg__w9Q9hfwCg-XHfYx7BLZw_hvxs_i2VAuSjts0Jx-azHzTQ5DQoLWacTXgCn8Zn09H7Yjg7ofBS1rLoYkeBWTtJWnvXhdprpQgs6dj42nFtWkDtkNpdo2kOYlyNnY_-nSEBLWP9FPbmizk-g1xFLUOFsZQyiNIpVymjQlNpVE2HscugWP826wdicT7f4rtNC9zCWNbVsq6Wdc3gzUb-qqfUuFHyhK2wkWIq7NSwWH61g2dZhaRmFY1zXS2EYZ0ROdntg6P-pzJ4RTa0g2te3_iqox2p6cfJdHvVku0yOFz3gq1YTXDLNKasTQZlsvdfVLKjybQ64ZJOKZ__-y1HcO_idGzPJ-2HA7hPIqavhzuEvdXyJ76Au_7Xana9fJm84DcpqATf |
| 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=Enhanced+CNN+for+image+denoising&rft.jtitle=CAAI+Transactions+on+Intelligence+Technology&rft.au=Tian%2C+Chunwei&rft.au=Xu%2C+Yong&rft.au=Fei%2C+Lunke&rft.au=Wang%2C+Junqian&rft.date=2019-03-01&rft.pub=John+Wiley+%26+Sons%2C+Inc&rft.eissn=2468-2322&rft.volume=4&rft.issue=1&rft.spage=17&rft.epage=23&rft_id=info:doi/10.1049%2Ftrit.2018.1054 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2468-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2468-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2468-2322&client=summon |