Crynodeb
Image super resolution is one of the most popular topics in the field of image processing. However, most of the existing super resolution algorithms are designed for the situation where sufficient training data is available. This paper proposes a new image super resolution approach that is able to handle the situation with sparse training data, using the recently developed ANFIS (Adaptive Network based Fuzzy Inference System) interpolation technique. In particular, the training image data set is divided into different subsets. For subsets with sufficient training data, the ANFIS models are trained using standard ANFIS learning procedure, while for those with insufficient data, the models are obtained through ANFIS interpolation. In the literature, little work exists for image super resolution on sparse data. Therefore, in the experimental evaluations of this paper, the proposed approach is compared with existing super resolution methods with full data, demonstrating that this work is able to produce highly promising results.
Iaith wreiddiol | Saesneg |
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Teitl | 2020 IEEE International Conference on Fuzzy Systems |
Is-deitl | FUZZ-IEEE |
Cyhoeddwr | IEEE Press |
ISBN (Electronig) | 9781728169323, 9781728169330 |
Dynodwyr Gwrthrych Digidol (DOIs) | |
Statws | E-gyhoeddi cyn argraffu - 26 Awst 2020 |
Digwyddiad | Fuzzy Systems - Glasgow, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon Hyd: 19 Gorff 2020 → 24 Gorff 2020 Rhif y gynhadledd: 29 |
Cyfres gyhoeddiadau
Enw | IEEE International Conference on Fuzzy Systems |
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Cyhoeddwr | IEEE |
Cyfrol | 2020 |
ISSN (Argraffiad) | 1098-7584 |
ISSN (Electronig) | 1558-4739 |
Cynhadledd
Cynhadledd | Fuzzy Systems |
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Teitl cryno | FUZZ-IEEE-2020 |
Gwlad/Tiriogaeth | Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon |
Dinas | Glasgow |
Cyfnod | 19 Gorff 2020 → 24 Gorff 2020 |