Adaptive Interpolation Scheme for Image Magnification Based on Local Fractal Analysis

Song, G; Qin, C; Zhang, K; Yao, X; Bao, F; Zhang, Y

Adaptive Interpolation Scheme for Image Magnification Based on Local Fractal Analysis

Song, G Qin, C Zhang, K Yao, X

Bao, F Zhang, Y

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Access, 2020, 8, pp. 34326-34338
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Song, G
dc.contributor.author	Qin, C
dc.contributor.author	Zhang, K
dc.contributor.author	Yao, X https://orcid.org/0000-0002-7475-0512
dc.contributor.author	Bao, F
dc.contributor.author	Zhang, Y
dc.date.accessioned	2021-05-25T00:00:42Z
dc.date.available	2021-05-25T00:00:42Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Access, 2020, 8, pp. 34326-34338
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/149183
dc.description.abstract	In this paper, we constructed a new rational fractal interpolation model with scale factors and shape parameters. The model obtains different expressions by changing the scale factor, according with the diversity of image features. On the basis of the constructed model, this paper presents a novel adaptive rational fractal magnification (ARFM) algorithm based on local fractal feature analysis. Firstly, the image is divided into different regions according to an adaptive threshold determined by a calculated fractal dimension, and the scaling factor is calculated based on the relationship between global fractal dimension (GFD) and local fractal dimension (LFD). Secondly, for different regions, different interpolation forms are selected according to regional characteristics. Thirdly, parameter optimization and sub-block selection are studied to further enhance the quality of the magnified image. The experimental results illustrate that the performance of the proposed ARFM algorithm is very competitive compared with the latest magnification algorithms.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2020.2966578
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Adaptive Interpolation Scheme for Image Magnification Based on Local Fractal Analysis
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0802 Computation Theory and Mathematics
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2021-05-25T00:00:38Z
pubs.publication-status	Published
pubs.volume	8

Abstract:

In this paper, we constructed a new rational fractal interpolation model with scale factors and shape parameters. The model obtains different expressions by changing the scale factor, according with the diversity of image features. On the basis of the constructed model, this paper presents a novel adaptive rational fractal magnification (ARFM) algorithm based on local fractal feature analysis. Firstly, the image is divided into different regions according to an adaptive threshold determined by a calculated fractal dimension, and the scaling factor is calculated based on the relationship between global fractal dimension (GFD) and local fractal dimension (LFD). Secondly, for different regions, different interpolation forms are selected according to regional characteristics. Thirdly, parameter optimization and sub-block selection are studied to further enhance the quality of the magnified image. The experimental results illustrate that the performance of the proposed ARFM algorithm is very competitive compared with the latest magnification algorithms.

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http://hdl.handle.net/10453/149183