Image super resolution with sparse data using ANFIS interpolation

Muhammad Ismail, Jing Yang, Changjing Shang, Qiang Shen

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

4 Dyfyniadau (Scopus)
168 Wedi eu Llwytho i Lawr (Pure)

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 wreiddiolSaesneg
Teitl2020 IEEE International Conference on Fuzzy Systems
Is-deitlFUZZ-IEEE
CyhoeddwrIEEE Press
ISBN (Electronig)9781728169323, 9781728169330
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsE-gyhoeddi cyn argraffu - 26 Awst 2020
DigwyddiadFuzzy Systems - Glasgow, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 19 Gorff 202024 Gorff 2020
Rhif y gynhadledd: 29

Cyfres gyhoeddiadau

EnwIEEE International Conference on Fuzzy Systems
CyhoeddwrIEEE
Cyfrol2020
ISSN (Argraffiad)1098-7584
ISSN (Electronig)1558-4739

Cynhadledd

CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2020
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
DinasGlasgow
Cyfnod19 Gorff 202024 Gorff 2020

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