1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPEW34M/45E82PS |
Repository | sid.inpe.br/sibgrapi/2021/09.14.11.28 |
Last Update | 2021:09.14.11.28.19 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2021/09.14.11.28.19 |
Metadata Last Update | 2022:09.10.00.16.17 (UTC) administrator |
Citation Key | NepomucenoSilv:2021:EvLoFu |
Title | Evaluating Loss Functions for Illustration Super-Resolution Neural Networks |
Format | On-line |
Year | 2021 |
Access Date | 2024, Mar. 29 |
Number of Files | 1 |
Size | 8458 KiB |
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2. Context | |
Author | 1 Nepomuceno, Raphael 2 Silva, Michel M. |
Affiliation | 1 Universidade Federal de Viçosa 2 Universidade Federal de Viçosa |
Editor | Paiva, Afonso Menotti, David Baranoski, Gladimir V. G. Proença, Hugo Pedro Junior, Antonio Lopes Apolinario Papa, João Paulo Pagliosa, Paulo dos Santos, Thiago Oliveira e Sá, Asla Medeiros da Silveira, Thiago Lopes Trugillo Brazil, Emilio Vital Ponti, Moacir A. Fernandes, Leandro A. F. Avila, Sandra |
e-Mail Address | raphael.nepomuceno@ufv.br |
Conference Name | Conference on Graphics, Patterns and Images, 34 (SIBGRAPI) |
Conference Location | Gramado, RS, Brazil (virtual) |
Date | 18-22 Oct. 2021 |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Book Title | Proceedings |
Tertiary Type | Undergraduate Work |
History (UTC) | 2021-09-14 11:28:19 :: raphael.nepomuceno@ufv.br -> administrator :: 2022-09-10 00:16:17 :: administrator -> :: 2021 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | super-resolution convolutional neural networks deep learning computer vision |
Abstract | As display technologies evolve and high-resolution screens become more available, the desirability of images and videos with high perceptual quality grows in order to properly utilize such advances. At the same time, the market for illustrated mediums, such animations and comics, has been in steady growth over the past years. Based on these observations, we were motivated to explore the super-resolution task in the niche of drawings. In absence of original high-resolution imagery, it is necessary to use approximate methods, such as interpolation algorithms, to enhance low-resolution media. Such methods, however, can produce undesirable artifacts in the reconstruct images, such as blurring and edge distortions. Recent works have successfully applied deep learning to this task, but such efforts are often aimed at real-world images and do not take in account the specifics of illustrations, which emphasize lines and employ simplified patterns rather than complex textures, which in turn makes visual artifacts introduced by algorithms easier to spot. With these differences in mind, we evaluated the effects of the choice of loss functions in order to obtain accurate and perceptually pleasing results in the super-resolution task for comics, cartoons, and other illustrations. Experimental evaluations have shown that a loss function based on edge detection performs best in this context among the evaluated functions, though still showing room for further improvements. |
Arrangement | urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Evaluating Loss Functions... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/45E82PS |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/45E82PS |
Language | en |
Target File | paper.pdf |
User Group | raphael.nepomuceno@ufv.br |
Visibility | shown |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/45PQ3RS |
Citing Item List | sid.inpe.br/sibgrapi/2021/11.12.11.46 1 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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