Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Svoboda, Pavel | |
dc.contributor.author | Hradiš, Michal | |
dc.contributor.author | Bařina, David | |
dc.contributor.author | Zemčík, Pavel | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2016-07-25T13:06:20Z | |
dc.date.available | 2016-07-25T13:06:20Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Journal of WSCG. 2016, vol. 24, no. 2, p. 63-72. | en |
dc.identifier.issn | 1213-6972 (print) | |
dc.identifier.issn | 1213-6980 (CD-ROM) | |
dc.identifier.issn | 1213-6964 (on-line) | |
dc.identifier.uri | http://wscg.zcu.cz/WSCG2016/!_2016_Journal_WSCG-No-2.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/21649 | |
dc.format | 10 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | cs |
dc.relation.ispartofseries | Journal of WSCG | en |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | hluboké učení | cs |
dc.subject | konvoluční neuronová síť | cs |
dc.subject | JPEG | cs |
dc.title | Compression artifacts removal using convolutional neural networks | en |
dc.type | článek | cs |
dc.type | article | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods. We were able to train networks with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and symmetric weight initialization. We provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives, generalization with respect to training dataset size, and generalization with respect to JPEG quality level. | en |
dc.subject.translated | deep learning | en |
dc.subject.translated | convolutional neural networks | en |
dc.subject.translated | JPEG | en |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | Volume 24, Number 2 (2016) |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Svoboda.pdf | Plný text | 2,67 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/21649
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