Sparse data-based image super-resolution with ANFIS interpolation

Muhammad Ismail, Changjing Shang*, Jing Yang, Qiang Shen

*Corresponding author for this work

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Image processing is a very broad field containing various areas, including image super-resolution (ISR) which re-represents a low-resolution image as a high-resolution one through a certain means of image transformation. The problem with most of the existing ISR methods is that they are devised for the condition in which sufficient training data is expected to be available. This article proposes a new approach for sparse data-based (rather than sufficient training data-based) ISR, by the use of an ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation technique. Particularly, a set of given image training data is split into various subsets of sufficient and sparse training data subsets. Typical ANFIS training process is applied for those subsets involving sufficient data, and ANFIS interpolation is employed for the rest that contains sparse data only. Inadequate work is available in the current literature for the sparse data-based ISR. Consequently, the implementations of the proposed sparse data-based approach, for both training and testing processes, are compared with the state-of-the-art sufficient data-based ISR methods. This is of course very challenging, but the results of experimental evaluation demonstrate positively about the efficacy of the work presented herein.

Original languageEnglish
Pages (from-to)7221-7233
Number of pages13
JournalNeural Computing and Applications
Issue number10
Early online date12 Sept 2021
Publication statusPublished - 01 Apr 2023


  • ANFIS interpolation
  • Image processing
  • Image super-resolution
  • Sparse data
  • S.I. : Neuro, fuzzy and their Hybridization
  • S.I: Fuzzy inference, neuro-fuzzy, and emerging fuzzy hybridization systems


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