Conditional Variational Autoencoder for Learned Image Reconstruction
Learned image reconstruction techniques using deep neural networks have recently gained popularity and have delivered promising empirical results. However, most approaches focus on one single recovery for each observation, and thus neglect information uncertainty. In this work, we develop a novel co...
Uloženo v:
| Vydáno v: | Computation Ročník 9; číslo 11; s. 114 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.11.2021
|
| Témata: | |
| ISSN: | 2079-3197, 2079-3197 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Learned image reconstruction techniques using deep neural networks have recently gained popularity and have delivered promising empirical results. However, most approaches focus on one single recovery for each observation, and thus neglect information uncertainty. In this work, we develop a novel computational framework that approximates the posterior distribution of the unknown image at each query observation. The proposed framework is very flexible: it handles implicit noise models and priors, it incorporates the data formation process (i.e., the forward operator), and the learned reconstructive properties are transferable between different datasets. Once the network is trained using the conditional variational autoencoder loss, it provides a computationally efficient sampler for the approximate posterior distribution via feed-forward propagation, and the summarizing statistics of the generated samples are used for both point-estimation and uncertainty quantification. We illustrate the proposed framework with extensive numerical experiments on positron emission tomography (with both moderate and low-count levels) showing that the framework generates high-quality samples when compared with state-of-the-art methods. |
|---|---|
| AbstractList | Learned image reconstruction techniques using deep neural networks have recently gained popularity and have delivered promising empirical results. However, most approaches focus on one single recovery for each observation, and thus neglect information uncertainty. In this work, we develop a novel computational framework that approximates the posterior distribution of the unknown image at each query observation. The proposed framework is very flexible: it handles implicit noise models and priors, it incorporates the data formation process (i.e., the forward operator), and the learned reconstructive properties are transferable between different datasets. Once the network is trained using the conditional variational autoencoder loss, it provides a computationally efficient sampler for the approximate posterior distribution via feed-forward propagation, and the summarizing statistics of the generated samples are used for both point-estimation and uncertainty quantification. We illustrate the proposed framework with extensive numerical experiments on positron emission tomography (with both moderate and low-count levels) showing that the framework generates high-quality samples when compared with state-of-the-art methods. |
| Author | Zhang, Chen Jin, Bangti Barbano, Riccardo |
| Author_xml | – sequence: 1 givenname: Chen surname: Zhang fullname: Zhang, Chen – sequence: 2 givenname: Riccardo surname: Barbano fullname: Barbano, Riccardo – sequence: 3 givenname: Bangti orcidid: 0000-0002-3775-9155 surname: Jin fullname: Jin, Bangti |
| BookMark | eNp9kE1Lw0AQhhepYK39A54Cnqv7vdljqV-FgiDqdZnsbkpKmq2bzcF_b2KKiIJzmWF43neY9xxNmtB4hC4JvmZM4xsb9ocuQapCownBhPATNKVY6QUjWk1-zGdo3rY73JcmLKd4im5XoXHVIIU6e4NYwXFedin4xgbnY1aGmG08xMa7bL2Hrc-evQ1Nm2JnB_wCnZZQt35-7DP0en_3snpcbJ4e1qvlZmFZLtJC8AIoWAeMSi6o5k5iKxQuixJcwQmIQjitqFdM5poVDFiZS5BK2dwR6dgMrUdfF2BnDrHaQ_wwASrztQhxayCmytbeYC21KCnR1HpOiQDgQmnBuOPMF3rwuhq9DjG8d75NZhe62H_eGioxxVQrlfcUHSkbQ9tGX35fJdgM4Zu_4fei_JfIViORIlT1f9JPVVePeg |
| CitedBy_id | crossref_primary_10_1109_TGRS_2025_3585865 crossref_primary_10_1088_1361_6560_ace49a crossref_primary_10_1016_j_neunet_2025_107740 crossref_primary_10_1109_ACCESS_2025_3594875 crossref_primary_10_1109_TIM_2025_3554283 crossref_primary_10_1109_RBME_2024_3485022 crossref_primary_10_1051_swsc_2025016 crossref_primary_10_1016_j_trc_2024_104618 |
| Cites_doi | 10.1088/1361-6420/ab15a3 10.1088/1361-6560/aac71a 10.1109/ICCV.2019.00335 10.1088/0266-5611/18/6/201 10.1109/MSP.2020.3016905 10.1561/2200000001 10.1109/ICPR48806.2021.9412521 10.1109/TMI.1982.4307558 10.1109/ISBI48211.2021.9433837 10.1007/s00466-019-01739-7 10.1016/0167-2789(92)90242-F 10.1142/9120 10.1088/1361-6420/aa9581 10.1109/ICNN.1994.374138 10.1109/TMI.2018.2805692 10.1007/978-3-319-46478-7_51 10.1007/978-3-319-10593-2_13 10.1109/TMI.2018.2886050 10.1023/A:1007665907178 10.1088/1361-6420/aaa0ab 10.1017/S0962492910000061 10.1137/19M1248352 10.1561/9781680836233 10.1162/neco.2008.08-07-592 10.1088/0031-9155/51/15/R01 10.1088/0266-5611/25/12/123010 10.1002/mp.12344 10.1109/JSAIT.2020.2991563 10.1016/B978-0-12-824349-7.00008-6 10.1016/j.inffus.2021.05.008 10.1214/aoms/1177729694 10.1109/TIP.2017.2662206 10.1007/b138659 |
| ContentType | Journal Article |
| Copyright | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7SC 7XB 8AL 8FD 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- L7M L~C L~D M0N P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U DOA |
| DOI | 10.3390/computation9110114 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) 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 Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic 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 ProQuest Central Basic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) Mathematics |
| EISSN | 2079-3197 |
| ExternalDocumentID | oai_doaj_org_article_09695f2192ce4215aa4579534d43eb9d 10_3390_computation9110114 |
| GroupedDBID | 5VS 8FE 8FG AADQD AAFWJ AAYXX ABUWG ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO GNUQQ GROUPED_DOAJ HCIFZ IAO K6V K7- KQ8 MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC 3V. 7SC 7XB 8AL 8FD 8FK JQ2 L7M L~C L~D M0N PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c385t-54ba2acda32645294d60c570fbfadb41a5b5d972e736893b3a3f86a677c8d16d3 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 19 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000725463100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2079-3197 |
| IngestDate | Fri Oct 03 12:53:21 EDT 2025 Sat Nov 01 15:16:17 EDT 2025 Sat Nov 29 07:14:53 EST 2025 Tue Nov 18 20:58:02 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c385t-54ba2acda32645294d60c570fbfadb41a5b5d972e736893b3a3f86a677c8d16d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-3775-9155 |
| OpenAccessLink | https://www.proquest.com/docview/2602029778?pq-origsite=%requestingapplication% |
| PQID | 2602029778 |
| PQPubID | 2032414 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_09695f2192ce4215aa4579534d43eb9d proquest_journals_2602029778 crossref_primary_10_3390_computation9110114 crossref_citationtrail_10_3390_computation9110114 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-11-01 |
| PublicationDateYYYYMMDD | 2021-11-01 |
| PublicationDate_xml | – month: 11 year: 2021 text: 2021-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Computation |
| PublicationYear | 2021 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | ref_50 Hyun (ref_6) 2018; 63 ref_57 ref_12 ref_56 ref_11 Opper (ref_36) 2009; 21 ref_55 Zhang (ref_1) 2017; 26 ref_51 Shepp (ref_52) 1982; 1 ref_19 ref_16 Cocosco (ref_49) 1997; 5 Stuart (ref_10) 2010; 19 ref_15 Kullback (ref_41) 1951; 22 Qi (ref_28) 2006; 51 Ongie (ref_32) 2020; 1 Kang (ref_4) 2017; 44 Jordan (ref_35) 1999; 37 ref_25 ref_24 ref_23 ref_22 ref_20 Zhou (ref_31) 2020; 13 ref_27 ref_26 Hou (ref_40) 2019; 64 Adler (ref_54) 2017; 33 Borcea (ref_13) 2002; 18 ref_34 Abdar (ref_21) 2021; 76 ref_33 Monga (ref_17) 2021; 38 Chen (ref_5) 2018; 37 ref_39 Wainwright (ref_18) 2008; 1 ref_38 Zhang (ref_29) 2019; 35 Arridge (ref_37) 2018; 34 Barat (ref_30) 2019; 38 ref_47 Rudin (ref_53) 1992; 60 ref_46 ref_45 ref_44 ref_43 ref_42 Arridge (ref_14) 2009; 25 ref_3 ref_2 ref_48 ref_9 ref_8 ref_7 |
| References_xml | – volume: 35 start-page: 085006 year: 2019 ident: ref_29 article-title: Expectation Propagation for Poisson Data publication-title: Inverse Probl. doi: 10.1088/1361-6420/ab15a3 – ident: ref_55 – ident: ref_26 – ident: ref_51 – ident: ref_16 – volume: 63 start-page: 135007 year: 2018 ident: ref_6 article-title: Deep learning for undersampled MRI reconstruction publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/aac71a – ident: ref_57 doi: 10.1109/ICCV.2019.00335 – ident: ref_42 – ident: ref_23 – volume: 18 start-page: R99 year: 2002 ident: ref_13 article-title: Electrical impedance tomography publication-title: Inverse Probl. doi: 10.1088/0266-5611/18/6/201 – volume: 38 start-page: 18 year: 2021 ident: ref_17 article-title: Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing publication-title: IEEE Signal Proc. Mag. doi: 10.1109/MSP.2020.3016905 – volume: 1 start-page: 1 year: 2008 ident: ref_18 article-title: Graphical models, exponential families, and variational inference publication-title: Found. Trends Mach. Learn. doi: 10.1561/2200000001 – ident: ref_24 doi: 10.1109/ICPR48806.2021.9412521 – volume: 1 start-page: 113 year: 1982 ident: ref_52 article-title: Maximum likelihood reconstruction for emission tomography publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.1982.4307558 – ident: ref_27 – ident: ref_39 doi: 10.1109/ISBI48211.2021.9433837 – volume: 64 start-page: 395 year: 2019 ident: ref_40 article-title: Solving Bayesian inverse problems from the perspective of deep generative networks publication-title: Comput. Mech. doi: 10.1007/s00466-019-01739-7 – ident: ref_48 – volume: 60 start-page: 259 year: 1992 ident: ref_53 article-title: Nonlinear total variation based noise removal algorithms publication-title: Phys. D. Nonlinear Phenom. doi: 10.1016/0167-2789(92)90242-F – ident: ref_47 doi: 10.1142/9120 – volume: 33 start-page: 124007 year: 2017 ident: ref_54 article-title: Solving ill-posed inverse problems using iterative deep neural networks publication-title: Inverse Probl. doi: 10.1088/1361-6420/aa9581 – ident: ref_38 – ident: ref_20 – ident: ref_56 doi: 10.1109/ICNN.1994.374138 – volume: 37 start-page: 1333 year: 2018 ident: ref_5 article-title: LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2018.2805692 – ident: ref_7 – ident: ref_45 doi: 10.1007/978-3-319-46478-7_51 – ident: ref_3 doi: 10.1007/978-3-319-10593-2_13 – volume: 38 start-page: 1643 year: 2019 ident: ref_30 article-title: PET reconstruction of the posterior image probability, including multimodal images publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2018.2886050 – ident: ref_34 – volume: 37 start-page: 183 year: 1999 ident: ref_35 article-title: An introduction to variational methods for graphical models publication-title: Mach. Learn. doi: 10.1023/A:1007665907178 – volume: 34 start-page: 025005 year: 2018 ident: ref_37 article-title: Variational Gaussian approximation for Poisson data publication-title: Inverse Probl. doi: 10.1088/1361-6420/aaa0ab – volume: 19 start-page: 451 year: 2010 ident: ref_10 article-title: Inverse problems: A Bayesian perspective publication-title: Acta Numer. doi: 10.1017/S0962492910000061 – ident: ref_11 – volume: 13 start-page: 29 year: 2020 ident: ref_31 article-title: Bayesian inference and uncertainty quantification for medical image reconstruction with Poisson data publication-title: SIAM J. Imaging Sci. doi: 10.1137/19M1248352 – ident: ref_43 doi: 10.1561/9781680836233 – volume: 21 start-page: 786 year: 2009 ident: ref_36 article-title: The variational Gaussian approximation revisited publication-title: Neural Comput. doi: 10.1162/neco.2008.08-07-592 – volume: 51 start-page: R541 year: 2006 ident: ref_28 article-title: Iterative reconstruction techniques in emission computed tomography publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/51/15/R01 – ident: ref_44 – volume: 25 start-page: 123010 year: 2009 ident: ref_14 article-title: Optical tomography: Forward and inverse problems publication-title: Inverse Probl. doi: 10.1088/0266-5611/25/12/123010 – volume: 5 start-page: S425 year: 1997 ident: ref_49 article-title: Brainweb: Online interface to a 3D MRI simulated brain database publication-title: NeuroImage – volume: 44 start-page: e360 year: 2017 ident: ref_4 article-title: A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction publication-title: Med. Phys. doi: 10.1002/mp.12344 – ident: ref_25 – ident: ref_50 – ident: ref_33 – volume: 1 start-page: 39 year: 2020 ident: ref_32 article-title: Deep learning techniques for inverse problems in imaging publication-title: IEEE J. Sel. Areas Inform. Theory doi: 10.1109/JSAIT.2020.2991563 – ident: ref_2 – ident: ref_46 – ident: ref_12 – ident: ref_8 doi: 10.1016/B978-0-12-824349-7.00008-6 – ident: ref_15 – volume: 76 start-page: 243 year: 2021 ident: ref_21 article-title: A review of uncertainty quantification in deep learning: Techniques, applications and challenges publication-title: Inf. Fusion doi: 10.1016/j.inffus.2021.05.008 – volume: 22 start-page: 79 year: 1951 ident: ref_41 article-title: On information and sufficiency publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177729694 – volume: 26 start-page: 3142 year: 2017 ident: ref_1 article-title: Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising publication-title: IEEE Trans. Imag. Proc. doi: 10.1109/TIP.2017.2662206 – ident: ref_19 – ident: ref_22 – ident: ref_9 doi: 10.1007/b138659 |
| SSID | ssj0000913820 |
| Score | 2.344547 |
| Snippet | Learned image reconstruction techniques using deep neural networks have recently gained popularity and have delivered promising empirical results. However,... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 114 |
| SubjectTerms | Artificial neural networks conditional variational autoencoder deep learning Epistemology Image reconstruction Inverse problems Machine learning Mathematical analysis Medical imaging Neural networks Positron emission Random variables Statistical methods Uncertainty uncertainty quantification |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA5SPOhBbFWsVsnBgyJLdzfvY60WPVg8qPS2ZPMAQVtpq7_fSXZbKwW9eF2yr8nM5Pvy-AahM6YsNxmQHA_ek1DifKI1BJ5PYSyWkmjubCw2IYZDORqph5VSX2FPWCUPXBmuCxBbMQ9xlRtHYXzSmjKhGKEWHlwqG7IvoJ4VMhVzsAraeml1SoYAr--aWCQh_i3Ed6ABP0aiKNi_lo_jIDPYRTs1OsS96quaaMONW2j7fimtOmuhZh2NM3xeS0Zf7KHr_iQsPcdpPfwM9Lee4sO9j_kkSFVaN8UAT3GUU3UW371BHsGBe34ryO6jp8HNY_82qesjJIZINk8YLXWujdUAwcL6KbU8NUykvvTaljTTrGRWidwJwgGWlEQTL7nmQhhpM27JAWqMJ2N3iLAH5CalyZjjjhoCuIwEYle6lKuSctdG2cJWhanFw0MNi9cCSESwb7Fu3za6XN7zXkln_Nr6KnTBsmWQvY4XwBmK2hmKv5yhjTqLDizqWJwVwNjyUKJLyKP_eMcx2srDvpZ4HrGDGtBJ7gRtms_5y2x6Gt3wCygU4hw priority: 102 providerName: Directory of Open Access Journals |
| Title | Conditional Variational Autoencoder for Learned Image Reconstruction |
| URI | https://www.proquest.com/docview/2602029778 https://doaj.org/article/09695f2192ce4215aa4579534d43eb9d |
| Volume | 9 |
| WOSCitedRecordID | wos000725463100001&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: 2079-3197 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913820 issn: 2079-3197 databaseCode: DOA dateStart: 20130101 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: 2079-3197 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913820 issn: 2079-3197 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2079-3197 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913820 issn: 2079-3197 databaseCode: P5Z dateStart: 20130301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2079-3197 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913820 issn: 2079-3197 databaseCode: K7- dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central (subscription) customDbUrl: eissn: 2079-3197 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913820 issn: 2079-3197 databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2079-3197 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913820 issn: 2079-3197 databaseCode: PIMPY dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6VlgMc6AMQC-3KBw4gFHUTP3NCfWxFhXYVVQUVLpFjOwgJNmWzcOS3M-P1boUq9dKLD4mjJJ6H5xvb3wC8lqVXLkeQ06L2ZIKHNrMWDa8d4VxsDLcq-FhsQk-n5uqqrFLCrU_bKlc-MTpq3znKkR9i3F1QoSVt3l__yqhqFK2uphIaD2AL35eTnn_U2TrHQpyXOMMtz8pwRPeHLpZKiP-MVk5g4L_5KNL23_LKcao5277vR-7AkxRksqOlVuzCRpjtwePJmqG134PdZNQ9e5OYp98-hdOTjlawY3aQfUYUnTKF7Oj3oiPGSx_mDKNcFllZg2fnP9EdMYKwN0S0z-DT2fjy5EOWyixkjhu5yKRobGGdtxjJ0TKs8GrkpB61TWt9I3IrG-lLXQTNFUY3Dbe8NcoqrZ3xufL8OWzOull4AazFANAYl8uggnAcwztO-LAJI1U2QoUB5KvBrl3iIKdSGD9qxCIkoPq2gAbwbv3M9ZKB487exyTDdU9iz44Xuvm3OhljjbCtlC366sIFgTGPtULqUnLhUVmb0g9gfyXeOpl0X9_I9uXdt1_Bo4I2vsQDi_uwicMfDuCh-7P43s-HsHU8nlYXwwj-h1FfsZ38HWNbya94vzqfVF_-AXjR91M |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NT9RAGH6DaKIeVFDjKuocNNGYhrbz2QMhCBI2CxsPaLiV6czUkMAWt6vGP8Vv5H1n2yWGhBsHr-1Mk848837NzPMAvJOFVy7DJKdG9CSChzqxFhdenaIvNoZbFXwUm9DjsTk6Kr4uwUV_F4aOVfY2MRpq3ziqka9j3J2T0JI2m-c_E1KNot3VXkJjDotR-PsHU7Z2Y7iD8_s-z3e_HG7vJZ2qQOK4kbNEisrm1nmLgQvtOgqvUid1Wle19ZXIrKykL3QeNFfozCtueW2UVVo74zPlOX73DtwV3Gji6h_pZFHTIY5N9KjzuzmcF-m6i9IMcYzRqlDy8Y__izIB17xAdG27j_-3QXkCj7ogmm3NUb8CS2GyCg8PFgy07SqsdEarZR86Zu2PT2Fnu6Ed-lj9ZN_t9KSrhLKtX7OGGD19mDKM4llknQ2eDc_Q3DJK0a-Idp_Bt1v5t-ewPGkm4QWwGgNcY1wmgwrCcQxfOeW_VUhVUQkVBpD1k1u6jmOdpD5OS8y1CBDldUAM4NOiz_mcYeTG1p8JM4uWxA4eHzTTH2VnbEpMSwtZoy_KXRAY01krpC4kFx4XY1X4Aaz1cCo7k9WWV1h6efPrt3B_7_Bgv9wfjkev4EFOh3zi5cw1WMapCK_hnvs9O2mnb-LqYHB828i7BGRFTjY |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Na9RAGH6pVUQPaqvFtVXnoKBI2CTzmYNI7bq4rC49qPQWJ_NRCrqpm1Xxr_nr-s7sZIsUeuvBazIJJPO8nzPzPADPeGWFKbDI8YiejFHnM63R8HyOsVgpqoWzUWxCzmbq6Kg63IC__VmYsK2y94nRUdvWhB75EPPuMggtSTX0aVvE4Wj85vRHFhSkwkprL6exgsjU_fmN5Vv3ejLCuX5eluN3nw7eZ0lhIDNU8WXGWaNLbazGJCasQDIrcsNl7huvbcMKzRtuK1k6SQUG9oZq6pXQQkqjbCEsxfdeg-sYhXmwsanM1v2dwLeJ0XV1TofSKh-aKNMQ_zd6mFCI_BMLo2TAhYgQw9z47v_8g-7BnZRck_2VNWzBhptvw-2Pa2babhu2kjPryIvEuP3yPowO2rByH7ui5ItenKQOKdn_uWwD06d1C4LZPYlstM6SyXd0wySU7ucEvA_g85V82w5sztu5ewjEY-KrlCm4E44ZimktDXVx43JRNUy4ART9RNcmca8HCZBvNdZgARz1RXAM4NX6mdMV88ilo98G_KxHBtbweKFdHNfJCdVYrlbcY4wqjWOY62nNuKw4ZRaNtKnsAPZ6aNXJlXX1Oa4eXX77KdxEwNUfJrPpLtwqw96feGZzDzZxJtxjuGF-LU-6xZNoKAS-XjXwzgC-T1bw |
| 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=Conditional+Variational+Autoencoder+for+Learned+Image+Reconstruction&rft.jtitle=Computation&rft.au=Zhang%2C+Chen&rft.au=Barbano%2C+Riccardo&rft.au=Jin%2C+Bangti&rft.date=2021-11-01&rft.pub=MDPI+AG&rft.eissn=2079-3197&rft.volume=9&rft.issue=11&rft.spage=114&rft_id=info:doi/10.3390%2Fcomputation9110114&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2079-3197&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2079-3197&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2079-3197&client=summon |