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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/45E82PS
Repositorysid.inpe.br/sibgrapi/2021/09.14.11.28
Last Update2021:09.14.11.28.19 (UTC) raphael.nepomuceno@ufv.br
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Citation KeyNepomucenoSilv:2021:EvLoFu
TitleEvaluating Loss Functions for Illustration Super-Resolution Neural Networks
FormatOn-line
Year2021
Access Date2022, Jan. 21
Number of Files1
Size8458 KiB
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Author1 Nepomuceno, Raphael
2 Silva, Michel M.
Affiliation1 Universidade Federal de Viçosa
2 Universidade Federal de Viçosa
EditorPaiva, 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 Addressraphael.nepomuceno@ufv.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2021-09-14 11:28:19 :: raphael.nepomuceno@ufv.br -> administrator ::
2021-11-12 11:47:10 :: administrator -> raphael.nepomuceno@ufv.br :: 2021
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordssuper-resolution
convolutional neural networks
deep learning
computer vision
AbstractAs 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.
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