L1 model-driven recursive multi-scale denoising network for image super-resolution
Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to the huge scale of the training data. However, it will bring a huge amount of weight...
Gespeichert in:
| Veröffentlicht in: | Knowledge-based systems Jg. 225; S. 1 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier B.V
05.08.2021
Elsevier Science Ltd |
| Schlagworte: | |
| ISSN: | 0950-7051, 1872-7409 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to the huge scale of the training data. However, it will bring a huge amount of weights and biases, and cost the expensive computations. Recently, some people have proposed a new frame for designing the deep networks according to the algorithms deduced from the ℓ2-optimization problem. But they did not consider the case with outliers. Since ℓ1-norm can describe the sparsity of the outliers better than ℓ2-norm, we propose an effective deep network designed according to the new algorithm deduced from the ℓ1-optimization problem. In our proposed method, an effective iterative algorithm for the ℓ1 reconstructed optimization problem is deduced based on the split Bregman algorithm, majorization–minimization algorithm, and soft thresholding operator. Then according to the deduced iterative algorithm, an effective deep network, named ℓ1 Model-Driven Recursive Multi-Scale Denoising Network (ℓ1-MRMDN), is designed. Due to the iteration form of the deduced algorithm, the proposed ℓ1-MRMDN contains an inner recursion and an outer recursion. Therefore, our proposed method can not only relieve its sensitiveness to the outliers because of the ℓ1 data fidelity term, but also avoid designing the deep network blindly via the guidance of prior knowledge. Extensive experimental results illustrate that our proposed method is superior to some related popular SISR methods. |
|---|---|
| AbstractList | Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to the huge scale of the training data. However, it will bring a huge amount of weights and biases, and cost the expensive computations. Recently, some people have proposed a new frame for designing the deep networks according to the algorithms deduced from the ℓ2-optimization problem. But they did not consider the case with outliers. Since ℓ1-norm can describe the sparsity of the outliers better than ℓ2-norm, we propose an effective deep network designed according to the new algorithm deduced from the ℓ1-optimization problem. In our proposed method, an effective iterative algorithm for the ℓ1 reconstructed optimization problem is deduced based on the split Bregman algorithm, majorization–minimization algorithm, and soft thresholding operator. Then according to the deduced iterative algorithm, an effective deep network, named ℓ1 Model-Driven Recursive Multi-Scale Denoising Network (ℓ1-MRMDN), is designed. Due to the iteration form of the deduced algorithm, the proposed ℓ1-MRMDN contains an inner recursion and an outer recursion. Therefore, our proposed method can not only relieve its sensitiveness to the outliers because of the ℓ1 data fidelity term, but also avoid designing the deep network blindly via the guidance of prior knowledge. Extensive experimental results illustrate that our proposed method is superior to some related popular SISR methods. |
| ArticleNumber | 107115 |
| Author | Sun, Zhongfan Zhou, Zhenghua Gao, Qingqing Zhao, Jianwei |
| Author_xml | – sequence: 1 givenname: Zhongfan surname: Sun fullname: Sun, Zhongfan organization: Department of Information Sciences and Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, PR China – sequence: 2 givenname: Jianwei orcidid: 0000-0001-9566-2178 surname: Zhao fullname: Zhao, Jianwei email: zhaojw@amss.ac.cn organization: Department of Information Sciences and Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, PR China – sequence: 3 givenname: Zhenghua surname: Zhou fullname: Zhou, Zhenghua organization: Department of Information Sciences and Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, PR China – sequence: 4 givenname: Qingqing surname: Gao fullname: Gao, Qingqing organization: Department of Information Sciences and Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, PR China |
| BookMark | eNotkE9rwzAMxc3oYG23b7CDYWd3sp3EzmUwyv5BYTC2s0kTpThN7c5OOvbt55JdJCEeT0-_BZk575CQWw4rDry471Z75-NvXAkQPK0U5_kFmXOtBFMZlDMyhzIHpiDnV2QRYwcAQnA9Jx8bTg--wZ41wZ7Q0YD1GGIa6WHsB8tiXfVIG3TeRut21OHw48Oetj5Qe6h2SON4xMACRt-Pg_Xumly2VR_x5r8vydfz0-f6lW3eX97WjxuGQuqBVbLWRd60CGVVqFJKVEKJttxqsZXY6gwaqABqVeSAOtumCiBFkeu2zoQUcknuJt9j8N8jxsF0fgwunTQiz3RRykJCUj1MKkxRThaDibVFV2Nj06uDabw1HMyZo-nMxNGcOZqJo_wDnDtqyA |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. Copyright Elsevier Science Ltd. Aug 5, 2021 |
| Copyright_xml | – notice: 2021 Elsevier B.V. – notice: Copyright Elsevier Science Ltd. Aug 5, 2021 |
| DBID | 7SC 8FD E3H F2A JQ2 L7M L~C L~D |
| DOI | 10.1016/j.knosys.2021.107115 |
| DatabaseName | Computer and Information Systems Abstracts Technology Research Database Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | Technology Research Database Computer and Information Systems Abstracts – Academic Library and Information Science Abstracts (LISA) ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-7409 |
| ExternalDocumentID | S0950705121003786 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 77K 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABAOU ABBOA ABIVO ABJNI ABMAC ABYKQ ACAZW ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADGUI ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W JJJVA KOM LG9 LY7 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SST SSV SSW SSZ T5K WH7 XPP ZMT ~02 ~G- 77I 7SC 8FD AATTM AAXKI AAYWO ACLOT ACVFH ADCNI AEIPS AEUPX AFPUW AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP E3H EFKBS F2A JQ2 L7M L~C L~D ~HD |
| ID | FETCH-LOGICAL-e238t-a3c865dfe09a67933e7272f9b82b3ef840d0a00c7650e84b50e0032658fc42323 |
| ISICitedReferencesCount | 22 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000659444400019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0950-7051 |
| IngestDate | Fri Nov 14 18:44:31 EST 2025 Fri Feb 23 02:44:08 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning Denoising network Super-resolution Iteration algorithm ℓ1 model-driven Majorization–minimization algorithm Soft thresholding operator |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-e238t-a3c865dfe09a67933e7272f9b82b3ef840d0a00c7650e84b50e0032658fc42323 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9566-2178 |
| PQID | 2548693630 |
| PQPubID | 2035257 |
| ParticipantIDs | proquest_journals_2548693630 elsevier_sciencedirect_doi_10_1016_j_knosys_2021_107115 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-08-05 20210805 |
| PublicationDateYYYYMMDD | 2021-08-05 |
| PublicationDate_xml | – month: 08 year: 2021 text: 2021-08-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationTitle | Knowledge-based systems |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V Elsevier Science Ltd |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier Science Ltd |
| References | J. Li, F. Fang, K. Mei, G. Zhang, Multi-scale residual network for image super-resolution, in: European Conference on Computer Vision, 2018, pp. 517-532. E.J. Candès, Compressive sampling, in: Proc. Int. Congr. Math., ICM 2006, vol. 3, 2006, pp. 1433-1452. J. Sun, J. Sun, Z. Xu, H.Y. Shum, Image super-resolution using gradient profile prior, in IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8. Y. Li, E. Agustsson, S. Gu, R. Timofte, L. Van, Carn: convolutional anchored regression network for fast and accurate single image super-resolution, in: European Conference on Computer Vision, 2018, pp. 166-181. Kim, Kwon (b12) 2010; 32 B. Lim, S. Son, S. Nah, K.M. Lee, Enhanced deep residual networks for single image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 1132-1140. C. Dong, C.C. Loy, K. He, X. Tang, Learning a deep convolutional network for image super-resolution, in: European Conference on Computer Vision, 2014, pp. 184-199. Chan, Wang, Elgendy (b34) 2016; 3 Zhang, Wu (b4) 2008; 17 norm regularized discriminative feature selection for unsupervised learning, in: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, 2011, pp. 1589-1594. Z. Hui, X. Wang, X. Gao, Fast and accurate single image super-resolution via information distillation network, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, pp. 723-731. Freeman, Jones, Pasztor (b11) 2002; 22 W. Shi, J. Caballero, F. Huszar, J. Totz, Z. Wang, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1874-1883. J. Kim, J.K. Lee, K.M. Lee, Deeply-recursive convolutional network for image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1637-1645. Fang, Zhou, Shen, Jacquemin, Shao (b10) 2020; 50 Natarajan (b31) 1995; 24 D.P. Kingma, J.L. Ba, Adam: A method for stochastic optimization, in: the 32nd International Conference on Machine Learning, ICML 2015, 2015, pp. 1-15. Yıldırım, Güngör (b2) 2012; 1 Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, Y. Fu, Image super-resolution using very deep residual channel attention networks, in: European Conference on Computer Vision, 2018, pp. 286-301. Bevilacqua, Roumy, Guillemot, Morel (b39) 2012; 135 Arbelaez, Maire, Fowlkes, Malik (b41) 2011; 33 Ren, He, Pu, Nguyen (b28) 2019; 99 Marquina, Osher (b6) 2008; 37 Y. Tai, J. Yang, X. Liu, C. Xu, MemNet: a persistent memory network for image restoration, in: IEEE International Conference on Computer Vision, 2017, pp. 4549-4557. Z. Hui, X. Gao, Y. Yang, X. Wang, Lightweight image super-resolution with information multi-distillation network, in: ACM International Conference on Multimedia, 2019, pp. 2024-2032. Wang, Chen, Hoi (b33) 2020; 99 Keys (b3) 1981; 29 Beck, Teboulle (b35) 2009; 2 R. Timofte, V. De Smet, L. Van Gool, A+: adjusted anchored neighborhood regression for fast super-resolution, in: Asian Conference on Computer Vision, vol. 9006, 2015, pp. 111-126. Zhang, Wu (b5) 2006; 15 Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2472-2481. Y. Tai, J. Yang, X. Liu, Image super-resolution via deep recursive residual network, in: IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2790-2798. J. Sun, J. Zhu, M.F. Tappen, Context-constrained hallucination for image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 231-238. J.B. Huang, A. Singh, N. Ahuja, Single image super-resolution from transformed self-exemplars, in: IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5197-5206. Hunter, Lange (b37) 2004; 58 Y. Yang, H.T. Shen, Z.G. Ma, H. Zi, X.F. Zhou W.Z. Shi, J. Caballero, C. Ledig, X.H. Zhuang, W.J. Bai, K. Bhatia, A.M.S.M. Marvao, T. Dawes, D. Regan, D. Rueckert, Cardiac image super-resolution with global correspondence using multi-atlas patchmatch, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 8151, 2013, pp. 9-16. C. Dong, C.C. Loy, X. Tang, Accelerating the super-resolution convolutional neural network, in: European Conference on Computer Vision, 2016, pp. 391-407. J. Kim, J.K. Lee, K.M. Lee, Accurate image super-resolution using very deep convolutional networks, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1646-1654. M. Haris, G. Shakhnarovich, N. Ukita, Deep back-projection networks for super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1664-1673. Goldstein, Osher (b36) 2009; 2 R. Timofte, S. Gu, L. Van Gool, L. Zhang, M.H. Yang, NTIRE 2018 challenge on single image super-resolution: methods and results, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 965-976. Wang, Wu, Pan (b9) 2014; 23 W.S. Lai, J.B. Huang, N. Ahuja, M.H. Yang, Deep Laplacian pyramid networks for fast and accurate super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5835-5843. Y.W. Li, S.H. Gu, K. Zhang, V.G. Luc, R. Timofte, DHP: differentiable meta pruning via hypernetworks, in: European Conference on Computer Vision, 2020, pp. 608-624. R. Zeyde, M. Elad, M. Protter, On single image scale-up using sparse-representations, in: International Conference on Curves and Surfaces, 2010, pp. 711-730. Dong, Wang, Yin, Shi, Wu, Lu (b29) 2019; 41 Zhang, Danelljan, Li, Timofte (b46) 2020 |
| References_xml | – volume: 24 start-page: 227 year: 1995 end-page: 234 ident: b31 article-title: Sparse approximate solutions to linear systems publication-title: SIAM J. Comput. – start-page: 1 year: 2020 end-page: 16 ident: b46 article-title: AIM 2020 challenge on efficient super-resolution: methods and results – reference: W. Shi, J. Caballero, F. Huszar, J. Totz, Z. Wang, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1874-1883. – reference: J.B. Huang, A. Singh, N. Ahuja, Single image super-resolution from transformed self-exemplars, in: IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5197-5206. – volume: 2 start-page: 323 year: 2009 end-page: 343 ident: b36 article-title: The split Bregman method for publication-title: SIAM J. Imaging Sci. – reference: Y. Li, E. Agustsson, S. Gu, R. Timofte, L. Van, Carn: convolutional anchored regression network for fast and accurate single image super-resolution, in: European Conference on Computer Vision, 2018, pp. 166-181. – reference: J. Kim, J.K. Lee, K.M. Lee, Accurate image super-resolution using very deep convolutional networks, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1646-1654. – volume: 23 start-page: 5123 year: 2014 end-page: 5135 ident: b9 article-title: Fast image upsampling via the displacement field publication-title: IEEE Trans. Image Process. – volume: 99 start-page: 1 year: 2019 end-page: 14 ident: b28 article-title: Learning image profile enhancement and denoising statistics priors for single-image super-resolution publication-title: IEEE Trans. Cybern. – volume: 22 start-page: 56 year: 2002 end-page: 65 ident: b11 article-title: Example-based super-resolution publication-title: IEEE Comput. Graph. Appl. – reference: R. Timofte, S. Gu, L. Van Gool, L. Zhang, M.H. Yang, NTIRE 2018 challenge on single image super-resolution: methods and results, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 965-976. – volume: 37 start-page: 367 year: 2008 end-page: 382 ident: b6 article-title: Image super-resolution by TV-regularization and bregman iteration publication-title: J. Sci. Comput. – reference: J. Li, F. Fang, K. Mei, G. Zhang, Multi-scale residual network for image super-resolution, in: European Conference on Computer Vision, 2018, pp. 517-532. – reference: B. Lim, S. Son, S. Nah, K.M. Lee, Enhanced deep residual networks for single image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 1132-1140. – reference: W.S. Lai, J.B. Huang, N. Ahuja, M.H. Yang, Deep Laplacian pyramid networks for fast and accurate super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5835-5843. – volume: 29 start-page: 1153 year: 1981 end-page: 1160 ident: b3 article-title: Cubic convolution interpolation for digital image processing publication-title: IEEE Trans. Acoust. – reference: Y.W. Li, S.H. Gu, K. Zhang, V.G. Luc, R. Timofte, DHP: differentiable meta pruning via hypernetworks, in: European Conference on Computer Vision, 2020, pp. 608-624. – reference: R. Zeyde, M. Elad, M. Protter, On single image scale-up using sparse-representations, in: International Conference on Curves and Surfaces, 2010, pp. 711-730. – reference: J. Kim, J.K. Lee, K.M. Lee, Deeply-recursive convolutional network for image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1637-1645. – reference: D.P. Kingma, J.L. Ba, Adam: A method for stochastic optimization, in: the 32nd International Conference on Machine Learning, ICML 2015, 2015, pp. 1-15. – reference: Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2472-2481. – volume: 33 start-page: 898 year: 2011 end-page: 916 ident: b41 article-title: Contour detection and hierarchical image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 41 start-page: 2305 year: 2019 end-page: 2318 ident: b29 article-title: Denoising prior driven deep neural network for image restoration publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 58 start-page: 30 year: 2004 end-page: 37 ident: b37 article-title: A tutorial on MM algorithms publication-title: Am. Stat. – volume: 1 start-page: 75 year: 2012 end-page: 83 ident: b2 article-title: A novel image fusion method using IKONOS satellite images publication-title: J. Geod. Geoinf. – volume: 135 start-page: 1 year: 2012 end-page: 10 ident: b39 article-title: Low-complexity single-image super-resolution based on nonnegative neighbor embedding publication-title: Br. Mach. Vis. Conf. – volume: 15 start-page: 2226 year: 2006 end-page: 2238 ident: b5 article-title: An edge-guided image interpolation algorithm via directional filtering and data fusion publication-title: IEEE Trans. Image Process. – volume: 50 start-page: 997 year: 2020 end-page: 1008 ident: b10 article-title: Text image deblurring using kernel sparsity prior publication-title: IEEE Trans. Cybern. – volume: 2 start-page: 183 year: 2009 end-page: 202 ident: b35 article-title: A fast iterative shrinkage-thresholding algorithm for linear inverse problems publication-title: SIAM J. Imaging Sci. – reference: C. Dong, C.C. Loy, K. He, X. Tang, Learning a deep convolutional network for image super-resolution, in: European Conference on Computer Vision, 2014, pp. 184-199. – reference: J. Sun, J. Sun, Z. Xu, H.Y. Shum, Image super-resolution using gradient profile prior, in IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8. – reference: Z. Hui, X. Gao, Y. Yang, X. Wang, Lightweight image super-resolution with information multi-distillation network, in: ACM International Conference on Multimedia, 2019, pp. 2024-2032. – reference: R. Timofte, V. De Smet, L. Van Gool, A+: adjusted anchored neighborhood regression for fast super-resolution, in: Asian Conference on Computer Vision, vol. 9006, 2015, pp. 111-126. – reference: Z. Hui, X. Wang, X. Gao, Fast and accurate single image super-resolution via information distillation network, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, pp. 723-731. – reference: Y. Tai, J. Yang, X. Liu, Image super-resolution via deep recursive residual network, in: IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2790-2798. – reference: Y. Yang, H.T. Shen, Z.G. Ma, H. Zi, X.F. Zhou, – volume: 3 start-page: 84 year: 2016 end-page: 98 ident: b34 article-title: Plug-and-play ADMM for image restoration: fixed-point convergence and applications publication-title: IEEE Trans. Comput. Imaging – volume: 32 start-page: 1127 year: 2010 end-page: 1133 ident: b12 article-title: Single-image super-resolution using sparse regression and natural image prior publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: C. Dong, C.C. Loy, X. Tang, Accelerating the super-resolution convolutional neural network, in: European Conference on Computer Vision, 2016, pp. 391-407. – reference: Y. Tai, J. Yang, X. Liu, C. Xu, MemNet: a persistent memory network for image restoration, in: IEEE International Conference on Computer Vision, 2017, pp. 4549-4557. – reference: E.J. Candès, Compressive sampling, in: Proc. Int. Congr. Math., ICM 2006, vol. 3, 2006, pp. 1433-1452. – volume: 17 start-page: 887 year: 2008 end-page: 896 ident: b4 article-title: Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation publication-title: IEEE Trans. Image Process. – volume: 99 start-page: 1 year: 2020 end-page: 22 ident: b33 article-title: Deep learning for image super-resolution: a survey publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: M. Haris, G. Shakhnarovich, N. Ukita, Deep back-projection networks for super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1664-1673. – reference: W.Z. Shi, J. Caballero, C. Ledig, X.H. Zhuang, W.J. Bai, K. Bhatia, A.M.S.M. Marvao, T. Dawes, D. Regan, D. Rueckert, Cardiac image super-resolution with global correspondence using multi-atlas patchmatch, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 8151, 2013, pp. 9-16. – reference: J. Sun, J. Zhu, M.F. Tappen, Context-constrained hallucination for image super-resolution, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 231-238. – reference: Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, Y. Fu, Image super-resolution using very deep residual channel attention networks, in: European Conference on Computer Vision, 2018, pp. 286-301. – reference: -norm regularized discriminative feature selection for unsupervised learning, in: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, 2011, pp. 1589-1594. |
| SSID | ssj0002218 |
| Score | 2.4190311 |
| Snippet | Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of... |
| SourceID | proquest elsevier |
| SourceType | Aggregation Database Publisher |
| StartPage | 1 |
| SubjectTerms | [formula omitted] model-driven Algorithms Data Deep learning Denoising Denoising network Fidelity Grammatical aspect Image resolution Iteration algorithm Iterative algorithms Iterative methods Machine learning Majorization–minimization algorithm Minimization Networks Noise reduction Optimization Outliers (statistics) Prior knowledge Recursion Soft thresholding operator Super-resolution |
| Title | L1 model-driven recursive multi-scale denoising network for image super-resolution |
| URI | https://dx.doi.org/10.1016/j.knosys.2021.107115 https://www.proquest.com/docview/2548693630 |
| Volume | 225 |
| WOSCitedRecordID | wos000659444400019&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7409 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLeg48CFb8TGQD5wQ54cO43t44TGZzUBG6jaJXISZ-0QzpY2sD-f54-kpb0AEhcrclPbfe_X9-X3bIRegAWeaM41cRx324wl0UwJIupSFaoap8ZXyH2diONjOZ2qjzGgv_DXCQhr5fW1uvyvrIY-YLYrnf0Ldg-DQgc8A9OhBbZD-0eMnyThehtStU6SvWxdRN0nqfvkQbIArrhiKdvMfZzAhkRwn284_-5SeBbdpWkJ-OFxnesG7Ic-Bkec_qviSdCDYX7SeSl2Nmvseb0C3tlMhx0eAONPM191N1143djzWTdoiDfh7U-wvKtetcbIBPOhVjpehcu2SmZi3JESQeMpsyZIXSnAzE-pWhfLLBREb4n4EG24OPhmG_iJB25i6BRJqArdODz7xE3nZgPPlnIhs5toh4mxkiO0c_juaPp-0NqM-VjwsLy-zNLnAm7P9ZvtsqHFvWlyeg_diT4FPgxYuI9uGPsA3e3v68BRfD9EnycJXocGHqCB16CBB2jgCA0M0MAeGngTGo_Ql9dHp6_ekninBjFgnC2J5qXMxlVtqNIZyGZu3E58rQrJCm5qcPcrqiktBVjuRqYFtEA4BnZqXbo9ff4YjWxjzROEU1764xmlBh8evlHAMAkXvEzhSTK9i0RPojyac8FMy4GVeZ9deJEH4uaOuHkg7i7a7ymax38WfA6-daZ4xunePw_8FN1eAXUfjZZtZ56hW-WP5XzRPo-Q-AU7u3yL |
| linkProvider | Elsevier |
| 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=L1+model-driven+recursive+multi-scale+denoising+network+for+image+super-resolution&rft.jtitle=Knowledge-based+systems&rft.au=Sun%2C+Zhongfan&rft.au=Zhao%2C+Jianwei&rft.au=Zhou%2C+Zhenghua&rft.au=Gao%2C+Qingqing&rft.date=2021-08-05&rft.pub=Elsevier+B.V&rft.issn=0950-7051&rft.eissn=1872-7409&rft.volume=225&rft_id=info:doi/10.1016%2Fj.knosys.2021.107115&rft.externalDocID=S0950705121003786 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0950-7051&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0950-7051&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0950-7051&client=summon |