Image Inpainting Using Wasserstein Generative Adversarial Imputation Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00351375" target="_blank" >RIV/68407700:21240/21:00351375 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-86340-1_46" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-86340-1_46</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-86340-1_46" target="_blank" >10.1007/978-3-030-86340-1_46</a>
Alternative languages
Result language
angličtina
Original language name
Image Inpainting Using Wasserstein Generative Adversarial Imputation Network
Original language description
Image inpainting is one of the important tasks in computer vision which focuses on the reconstruction of missing regions in an image. The aim of this paper is to introduce an image inpainting model based on Wasserstein Generative Adversarial Imputation Network. The generator network of the model uses building blocks of convolutional layers with different dilation rates, together with skip connections that help the model reproduce fine details of the output. This combination yields a universal imputation model that is able to handle various scenarios of missingness with sufficient quality. To show this experimentally, the model is simultaneously trained to deal with three scenarios given by missing pixels at random, missing various smaller square regions, and one missing square placed in the center of the image. It turns out that our model achieves high-quality inpainting results on all scenarios. Performance is evaluated using peak signal-to-noise ratio and structural similarity index on two real-world benchmark datasets, CelebA faces and Paris StreetView. The results of our model are compared to biharmonic imputation and to some of the other state-of-the-art image inpainting methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks and Machine Learning – ICANN 2021
ISBN
978-3-030-86339-5
ISSN
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e-ISSN
1611-3349
Number of pages
12
Pages from-to
575-586
Publisher name
Springer
Place of publication
Cham
Event location
Bratislava
Event date
Sep 14, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000711922300046