Image super resolution with sparse data using ANFIS interpolation

Muhammad Ismail, Jing Yang, Changjing Shang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

4 Citations (SciVal)
150 Downloads (Pure)


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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Fuzzy Systems
Subtitle of host publicationFUZZ-IEEE
PublisherIEEE Press
ISBN (Electronic)9781728169323, 9781728169330
Publication statusE-pub ahead of print - 26 Aug 2020
EventFuzzy Systems - Glasgow, United Kingdom of Great Britain and Northern Ireland
Duration: 19 Jul 202024 Jul 2020
Conference number: 29

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584
ISSN (Electronic)1558-4739


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2020
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
Period19 Jul 202024 Jul 2020


  • ANFIS Interpolation
  • Image Super Resolution
  • Sparse Training Data


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