Single frame image super resolution using ANFIS interpolation: An initial experiment-based approach

Muhammad Ismail; Jing Yang; Changjing Shang; Qiang Shen

doi:10.1007/978-3-030-29933-0_3

Single frame image super resolution using ANFIS interpolation: An initial experiment-based approach

Muhammad Ismail, Jing Yang, Changjing Shang, Qiang Shen

Department of Computer Science

Sukkur IBA University

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

2 Citations (Scopus)

64 Downloads (Pure)

Abstract

Image super resolution is a classical problem in image processing. Different from most of the existing super resolution algorithms that work on sufficient training data, in this work, a new super resolution method is proposed to handle the situation where the training data is insufficient by the use of ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation. ANFIS interpolation aims to interpolate an effective ANFIS given only sparse data in the problem area of interest, with the assistance of two well trained ANFISs in the neighbourhood areas. The interpolated ANFIS constructs mappings from low resolution images to high resolution ones, which provides an effective mechanism for further inference of high resolution images from given low resolution ones. Experimental results indicate that the proposed approach entails improved super resolution performance for situations where there is a shortage of training data.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019
Editors	Zhaojie Ju, Dalin Zhou, Alexander Gegov, Longzhi Yang, Chenguang Yang
Publisher	Springer Nature
Pages	27-40
Number of pages	14
Volume	1043
ISBN (Electronic)	978-3-030-29933-0
ISBN (Print)	978-3-030-29932-3
DOIs	https://doi.org/10.1007/978-3-030-29933-0_3
Publication status	Published - 30 Aug 2019
Event	UK Workshop on Computational Intelligence - University of Portsmouth, Portsmouth, United Kingdom of Great Britain and Northern Ireland Duration: 04 Sept 2019 → 05 Sept 2019 Conference number: 19 https://www.ukci2019.port.ac.uk/

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1043
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Workshop

Workshop	UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2019
Country/Territory	United Kingdom of Great Britain and Northern Ireland
City	Portsmouth
Period	04 Sept 2019 → 05 Sept 2019
Internet address	https://www.ukci2019.port.ac.uk/

Keywords

Image Super Resolution
Insufficient training data
ANFIS interpolation
Image super resolution

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10.1007/978-3-030-29933-0_3

Accepted ManuscriptAccepted author manuscript, 5.15 MB

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Ismail, M., Yang, J., Shang, C., & Shen, Q. (2019). Single frame image super resolution using ANFIS interpolation: An initial experiment-based approach. In Z. Ju, D. Zhou, A. Gegov, L. Yang, & C. Yang (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019 (Vol. 1043, pp. 27-40). (Advances in Intelligent Systems and Computing; Vol. 1043). Springer Nature. https://doi.org/10.1007/978-3-030-29933-0_3

Ismail, Muhammad ; Yang, Jing ; Shang, Changjing et al. / Single frame image super resolution using ANFIS interpolation : An initial experiment-based approach. Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. editor / Zhaojie Ju ; Dalin Zhou ; Alexander Gegov ; Longzhi Yang ; Chenguang Yang. Vol. 1043 Springer Nature, 2019. pp. 27-40 (Advances in Intelligent Systems and Computing).

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abstract = "Image super resolution is a classical problem in image processing. Different from most of the existing super resolution algorithms that work on sufficient training data, in this work, a new super resolution method is proposed to handle the situation where the training data is insufficient by the use of ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation. ANFIS interpolation aims to interpolate an effective ANFIS given only sparse data in the problem area of interest, with the assistance of two well trained ANFISs in the neighbourhood areas. The interpolated ANFIS constructs mappings from low resolution images to high resolution ones, which provides an effective mechanism for further inference of high resolution images from given low resolution ones. Experimental results indicate that the proposed approach entails improved super resolution performance for situations where there is a shortage of training data.",

keywords = "Image Super Resolution, Insufficient training data, ANFIS interpolation, Image super resolution",

author = "Muhammad Ismail and Jing Yang and Changjing Shang and Qiang Shen",

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Ismail, M, Yang, J, Shang, C & Shen, Q 2019, Single frame image super resolution using ANFIS interpolation: An initial experiment-based approach. in Z Ju, D Zhou, A Gegov, L Yang & C Yang (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. vol. 1043, Advances in Intelligent Systems and Computing, vol. 1043, Springer Nature, pp. 27-40, UK Workshop on Computational Intelligence, Portsmouth, United Kingdom of Great Britain and Northern Ireland, 04 Sept 2019. https://doi.org/10.1007/978-3-030-29933-0_3

Single frame image super resolution using ANFIS interpolation: An initial experiment-based approach. / Ismail, Muhammad; Yang, Jing; Shang, Changjing et al.
Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. ed. / Zhaojie Ju; Dalin Zhou; Alexander Gegov; Longzhi Yang; Chenguang Yang. Vol. 1043 Springer Nature, 2019. p. 27-40 (Advances in Intelligent Systems and Computing; Vol. 1043).

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

TY - GEN

T1 - Single frame image super resolution using ANFIS interpolation

T2 - UK Workshop on Computational Intelligence

AU - Ismail, Muhammad

AU - Yang, Jing

AU - Shang, Changjing

AU - Shen, Qiang

N1 - Conference code: 19

PY - 2019/8/30

Y1 - 2019/8/30

N2 - Image super resolution is a classical problem in image processing. Different from most of the existing super resolution algorithms that work on sufficient training data, in this work, a new super resolution method is proposed to handle the situation where the training data is insufficient by the use of ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation. ANFIS interpolation aims to interpolate an effective ANFIS given only sparse data in the problem area of interest, with the assistance of two well trained ANFISs in the neighbourhood areas. The interpolated ANFIS constructs mappings from low resolution images to high resolution ones, which provides an effective mechanism for further inference of high resolution images from given low resolution ones. Experimental results indicate that the proposed approach entails improved super resolution performance for situations where there is a shortage of training data.

AB - Image super resolution is a classical problem in image processing. Different from most of the existing super resolution algorithms that work on sufficient training data, in this work, a new super resolution method is proposed to handle the situation where the training data is insufficient by the use of ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation. ANFIS interpolation aims to interpolate an effective ANFIS given only sparse data in the problem area of interest, with the assistance of two well trained ANFISs in the neighbourhood areas. The interpolated ANFIS constructs mappings from low resolution images to high resolution ones, which provides an effective mechanism for further inference of high resolution images from given low resolution ones. Experimental results indicate that the proposed approach entails improved super resolution performance for situations where there is a shortage of training data.

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KW - Insufficient training data

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M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-030-29932-3

VL - 1043

T3 - Advances in Intelligent Systems and Computing

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BT - Advances in Computational Intelligence Systems

A2 - Ju, Zhaojie

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A2 - Yang, Chenguang

PB - Springer Nature

Y2 - 4 September 2019 through 5 September 2019

ER -

Ismail M, Yang J, Shang C , Shen Q. Single frame image super resolution using ANFIS interpolation: An initial experiment-based approach. In Ju Z, Zhou D, Gegov A, Yang L, Yang C, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. Vol. 1043. Springer Nature. 2019. p. 27-40. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-29933-0_3

Single frame image super resolution using ANFIS interpolation: An initial experiment-based approach

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